monojit, Author at Adamas University

Tips-For Successful career in Engineering

All engineering aspirants have a goal of successful career. ‘Successful’ engineering career don’t happen by chance. Students need to be trained so that they relish an evolving and successful career in their profession. Here few pointers are discussed for a fresher engineers who are at the beginning of their career-

Think like an entrepreneur:

Engineering students are satisfied with their technical job roles and responsibilities. They are not eager to step out from their comfort zone. However, today industries and businesses are competing for more revenue and profit. They are looking for professionals who are not only technically sound but also ready to take up additional job responsibilities. They should have problem solving skill so that they are able to make strategy to maximize profit and help their company grow. Therefore engineering students need to develop their thinking skill like entrepreneurs in this highly competitive world.

Commonly used Methodology for Problem Solving

Out of Box thinking:

To enhance the chances of being promoted in new, different and challenging assignments only theoretical and practical knowledge of engineering is not enough. If students restrict themselves to a single technical role, it is difficult for them to step into newer superior job roles. By thinking out of the box students can prove themselves as a flexible professional in front of their recruiters. It should be remembered that ‘Innovative Thinking’ lies side by side with the job roles of an engineer.

Be a Team worker:

Engineering is a combination of different sub disciplines and specializations. Therefore, for making any project successful in any industry collaborative work process is needed. Recently nearly every company has more or less taken up the practice of team work and coordination which develop the pillars of proper work flow for engineers. The teamwork skills that employers look for in their future employees are cooperation , contribution, persuasion, reliability, respectfulness , subordination, communication , conflict-resolution, decision making and tolerance.

Importance of Team Work

Keep learning and up skilling:

Technology is the field of research and invention and it is constantly changing. Therefore one needs to be updated continuously. New generation fresh engineers are entering into the workspace every year. They are young and more skilled. Thus it is essential for engineering professional to make themselves compatible with new generation and update themselves with new skillset. It is seen that up skilling increases the confidence at work.

Be innovative:

Innovation is the key driver for today’s engineering world. Innovation and Technology always go hand in hand. When engineers use their skills beyond their work for the welfare of society, we can say they are innovative. Recent companies favor innovative engineers more in their workspace. In this post pandemic era as the human race becoming more and more challenging, engineers are pushed to be innovative. If someone is planning to stay in the same company or industry for a long time, innovation is more important to sustain a long career.

Work hard and be optimistic:

“Genius,” Thomas Edison once said, “is two percent inspiration, ninety-eight percent perspiration.” Though the collage life requires lots of sweat, but the real life started at work spaces. People want highly reliable and easy to use technology. Engineers have to work hard to make the things simple and mass adoptable. To give reliable solution to the complex problem is the real challenge. Though it is difficult to balance optimism with a grasp on reality, if an engineer aspires to lead a company or industry, he should cultivate optimism. As an engineer who is just stepping into the professional world, students at the Adamas University are also trained by their teachers and mentors so that they enjoy an evolving and successful career in their profession.

BIOMEDICAL ENGINEERs – Warriors of all time

In COVID-19 crisis, the real warriors were the Biomedical Engineers. They played an important role in medical technology in patient care. They involved themselves in making ventilators and PPE (personal protection equipment) kits to help the COVID patients. Biomedical engineers focus on inventing new devices and develop modern technologies which help in improving human health care system. With the help of the doctors and researchers, Biomedical Engineers are developing equipments to solve clinical problems.

Electing biomedical engineering as métier is extremely rewarding. Biomedical Engineers has the ability to save lives through innovation and modern technology. That’s why experts from Human Resource Department’s planning team suggest that every doctor along with medical studies should be accustomed with biomedical engineering studies.

Biomedical engineering, also known as medical engineering, is a form of engineering associated with the study in the fields of biology and health care system.

The following qualities are required for Biomedical Engineers:

Analytical skills
Communication skills
Listening skills
Math skills

Apart from these skills, they should have idea of all disciplines ranging from material science to electronics, life science to biomechanics and mathematics to computation. Because of these, Biomedical Engineering is completely interdisciplinary in nature and the Biomedical Engineers possess vast knowledge across of all domains.

AI Washing – Analysing industrial impacts

By 2020, Artificial Intelligence (AI) and related technologies will be found in a wide range of businesses, in a large number of software packages, and in our daily lives. AI will be one of the top five investment priorities for at least 30% of Chief Information Officers by 2020. This new gold rush has attracted global software manufacturers. Unfortunately, while the prospect of additional revenue has driven software company owners to invest in AI technology, the reality is that most companies lack the specialised personnel required to embrace AI.

An implicit point of warning in many industry surveys on AI, Machine Learning and its impact on current industries is that software developers should first focus on understanding the needs of the customer and potential benefits from AI, before chasing the hype, which has been called as “AI Washing”.

The current trust deficit in “capabilities of tech-enabled solutions” will diminish in the next ten years.

The impact of AI in the coming decade

Over the next decade, we’ll see a dramatic transition from scepticism and partial suspicion to complete reliance on AI and other advanced technology. The majority of AI-powered applications are aimed at consumers, which is another compelling reason for mainstream users to overcome their distrust over time. The Citizen Data Science community will pave the path for a new technological order by gaining more exposure and access to technological solutions for their daily activities.

While technologies like the cloud computing allow business processes to be more agile, AI and Machine Learning can influence business outcomes. People have sought to construct a machine that behaves like a person in the post-industrial period. The thinking machine is AI’s greatest gift to humanity; the arrival of this self-propelled machine has completely altered the business landscape. Self-driving cars, digital assistants, robotic factory workers, and smart cities have all demonstrated that intelligent robots are viable in recent years. AI has altered almost every industry sector, including retail, manufacturing, finance, healthcare, and media, and it is still expanding.

The Future of Machine Learning

Based on current technology and developments we can assume that all AI systems, large or small, will include some form of machine learning.As machine learning becomes more important in corporate applications, it is likely that this technology will be delivered as a Cloud-based service known as Machine Learning-as-a-Service (MLaaS).Connected AI systems will allow ML algorithms to actively learn from new emerging data in the internet.Hardware suppliers will be rushing to increase CPU power to handle ML data processing. Hardware vendors will be driven to alter their computers to better accommodate the capabilities of machine learning.

Some Predictions about Machine Learning

Multiple Technologies in Machine Learning: In many ways, the Internet of Things has benefited Machine Learning. Variousalgorithms are now being used in machine learning to improve learning capabilities and collaborative learning using multiple algorithms is likely in the future.
ML Developers will have access to APIs to develop and deploy “smart applications” in a personalised computing environment. This resembles “assisted programming” in someways. Developers may simply integrate facial, speech, and visual recognition features into their applications using these API kits.
Quantum computing will dramatically improve the speed with which machine learning algorithms in high-dimensional vector processing are executed. This will be the next major breakthrough in machine learning research.
Future advancement in “unsupervised ML algorithms” will lead to higher business outcomes.
Tuned Recommendation Engines: In the future, ML-enabled services will be more accurate and relevant. Recommendation Engines in the future, for example, will be significantly more relevant and tailored to a user’s unique likes and tastes.

Will AI and ML impact the cyber security industry?

According to current AI and ML research trends, advances in cyber-security have advanced ML algorithms to the next level of learning, implying that future security-centric AI and ML applications will be distinguished by their speed and accuracy. Machine Learning, Artificial Intelligence, and the Future of Cyber Security contains the complete story. This emerging practise could help Data Scientists and cyber security specialists work together to achieve common software development goals.

Benefiting Humanity: AI and ML in Core Industry Sectors

It’s difficult to overlook the global impact of “AI Washing” in today’s commercial market, as well as how AI and machine learning may transform application development markets in the future.

AI and machine learning have often been compared to the discovery of electricity at the beginning of the Industrial Revolution. These cutting-edge technologies, like electricity, have ushered in a new age in information technology history.

Today, AI and machine learning-powered platforms are transforming the way businesses are conducted across all industries. These cutting-edge technologies are progressively bringing about dramatic changes in a variety of industries, including the following:

Healthcare:Human practitioners and robots will gradually work together to improve outcomes. Smart AI enabled equipment would be expected to provide faster and accurate diagnoses of patient ailments, allowing practitioners to attend more number of patients.

Financial Services : The article AI and Machine Learning are the New Future Technology Trends looks at how new technologies such as blockchain are affecting India’s capital markets. Capital-market operators, for example, can use blockchain to forecast market movements and detect fraud. AI technologies not only open up new business models in the financial sector, but they also strengthen the position of AI technologists in the business-investment ecosystem.

Real Estate : Contactually.com, an innovative CRM system for the real estate industry, was created exclusively to link investors with entrepreneurs in Washington, DC. Computer Learning algorithms add to the power of the static system, transforming it into a live, interactive machine that listens, approves, and suggests.

Administration of Databases : AI technology can automate procedures and duties in a typical DBA system because of the repeated tasks. Nowadays DBA is equipped with modern AI based algorithms so that they may make value-added contributions to their organisations rather than just executing routine jobs.

Personal Devices : According to several analysts, AI represents a game changer for the personal device sector. By 2020, AI-enabled Cloud platforms will be used by around 60% of personal-device technology manufacturers to supply better functionality and personalised services. Artificial intelligence will provide an “emotional user experience.”

A Green Career Awaits You after Studying Environmental Engineering during B. Tech Civil Engineering: A Wide Opportunity after COVID-19 Pandemic

Covid-19 pandemic has changed the academic sector greatly in different aspects, like teaching-learning process, student engagement, evaluation methodology, students’ placement activities etc. During pandemic, rapid changes in job market created certain fear in the minds of budding Engineers; job opportunities started getting restricted to some extent. After the third wave of pandemic, the situation is getting improved now and we are again moving towards “Normal” life from the “New normal”. The job opportunities are getting rejuvenated in Core Engineering sector again.

Now, directly moving to discuss about the topic, I need to throw some light on the post pandemic career opportunities after completing B. Tech in Civil Engineering. Lots of opportunities are getting regenerated after pandemic times in industrial sectors, consultancy, research and development sectors in Civil Engineering domain. After getting B. Tech degree, a fresher Civil Engineer can be employed in industries as structural designing engineering or a detailing engineer; employment opportunities are there in construction fields as site engineer or geotechnical engineer; students can have analytical jobs in the areas like Quality control in concrete, Soil laboratory etc. Besides these common job opportunities, a newer trend is developing in India to work on Environmental fields. Civil Engineering is a well-known trade which includes Environmental Engineering as an important subject in B. Tech curriculum. Lots of career options are coming out for a fresher Civil Engineer after studying Environmental Engineering during a B. Tech Civil Engineering program.

Environmental Engineering is a burning topic now in the whole world. In India also, the term is getting more relevant day by day. It is the engineering trade which deals with the Environment, Sustainability and Mother Earth. It is related to every moment of our lives as we breathe, drink, live and react in the environment. Poor ambient air quality in metro cities, contamination of river water, excessive noise pollution are clearly showing that without suitable engineering approaches against environmental pollution our future generation will not survive contentedly. That is why the need to adopt Environmental Engineering practices is becoming popular and unavoidable in today’s society. Environmental issues like Global warming, Ozone Layer Depletion, Green House Gas emissions, Climate change are being highlighted very frequently nowadays; and such issues are making the Government and non-government sectors to think about its possible solutions to move towards a green and sustainable future. Such sustainable thoughts are in fact creating enormous job opportunities for Civil Engineering students. Various research activities are emerging to solve global environmental concerns; product developments are also being going on in a wide scale to solve our day-to-day problems related to environment. Such a trade can easily attract the scholars to work in the field of Environment to achieve a sustainable future for mankind. A fresher Civil Engineer is having a broad opening to work in such a field, because the Civil Engineers create and build important elements of the society which needs to be sustainable. From Building construction to Road making, from Water treatment plant construction to design of sanitation system, from Green building to Air pollution control, all are the parts of a society. A Civil Engineer can be involved in all of these and today the thinking of sustainability needs to be adopted in all these. So, the knowledge and skill achieved by studying Environmental Engineering can surely widen the career path of all Civil Engineering degree holders and improve the placement statistics in academic sector to a great extent.

Let’s have some clear understanding about the scope of Civil Engineering students in Environmental jobs. In India, the job opportunities are increasing in environmental domain in recent years. In industries, environmental engineers are required to look into all environmental management related works. Such environmental management works includes water quality monitoring, ambient air quality monitoring, noise level monitoring, Waste management and other statutory requirements based on types of industries. A B. Tech Civil Engineering student can acquire detailed knowledge about the mentioned domain through M. Tech Course in Environmental Engineering, which can even broaden the employment opportunities directly to the industries for Environmental Management positions. Contaminated site management is another scope to work in industries. In certain containment area design, which requires sound environmental knowledge, Civil Engineers can take part in industries. Say for example, in Power plants huge quantity of fly ash gets generated and stored in Containment areas, which needs to be properly designed, constructed, modified and monitored. Civil Engineering plays the major role in such activities to ensure zero environmental pollution from such Ash storage areas. For starting a new industrial project certain norms to be addressed by the Project Engineers; in such cases Civil Engineers with sound environmental knowledge can participate in consultation with environmental experts. In all the steps of Civil Engineering Projects in Industries, environmental regulation should be kept in mind. So, environmentally sound construction is the future for the Civil Engineers to achieve a green and sustainable future.

Besides industrial sectors, environmental engineering knowledge can help civil engineers in many other areas and can make them relevant and essential in various public and private projects. Such projects may be solely depending on the skills of civil engineers for upgrading infrastructure and the environment in a society. For planning, designing, and constructing environmental pollution controlling systems, storm water drainage systems, water treatment plants, water distribution systems, Sewage treatment processes, Civil engineers play the important role to enhance the quality of life and to maintain environmental quality and public health aspects. In all the stages of such projects, impacts on environment and society should be taken into consideration and from planning to execution Civil engineers need to show environmental quality control approach. In laboratories, Civil engineers can work as environmental analysts with suitable eligibility criteria. In Government sectors, Assistant Engineer and Scientists positions are available specifically in Environmental domain under Pollution Control Boards. In private, Civil Engineering students can go for consultancy jobs which are specifically related to Environmental system designs. Rain water harvesting is a burning area where Civil engineers can easily prosper for their future. Green Building is also an emerging concept where Civil Engineers can get directly absorbed. Green Buildings are planned, designed, executed and maintained in a way which environmentally harmless to ensure reduced environmental impacts with resource-efficient thoughts. Certain Certifications can also help the Civil engineers to grow further in Green Building construction. So, lots of career opportunities are available in Environmental domain after completing B. Tech in Civil Engineering and going forward with suitable higher studies on Environment.

That’s all from my side! I hope this will help you to get a clear view about the possible career opportunities available in Environmental Engineering fields after studying Civil Engineering in B. Tech Course.

Thank you. Stay healthy, stay safe.

Real-Time SKU detection in the browser using TensorFlow.js

Summary: To build an efficient machine learning model for the consumer goods companies to ensure that their products are available and properly placed in stores.

The problem:

Items that are often eaten by consumers (foods, beverages, household supplies, etc.) necessitate a detailed replenishment and positioning routine at the point of sale (supermarkets, convenience stores, etc).

Researchers have frequently demonstrated over the last few years that around two-thirds of purchase choices are made after buyers enter the store. One of the most difficult tasks for consumer goods companies is to ensure that their products are available and properly placed in stores.

Teams in stores organise shelves based on marketing objectives and maintain product levels in stores. These individuals may count the number of SKUs of each brand in a store to estimate product stockpiles and market share, as well as assist in the development of marketing plans.

Preparing the data:

Gathering good data is the first step in training a decent model. As previously said, this solution will employ a dataset of SKUs in various scenarios. SKU110K was created to serve as a benchmark for models that can recognise objects in densely packed settings.

The dataset is in Pascal VOC format, which must be translated to tf.record. The conversion script can be found here, and the tf.record version of the dataset can be found in my project repository. As previously said, SKU110K is a vast and difficult dataset to work with. It has a large number of objects that are often similar, if not identical, and are arranged in close proximity.

Choosing the model:

The SKU detection problem can be solved using a number of neural networks. However, when translated to TensorFlow.js and run in real-time, the architectures that readily reach a high level of precision are quite dense and do not have tolerable inference times.

As a result, the focus here will be on optimising a mid-level neural network to attain respectable precision while working on densely packed scenes and running inferences in real-time. The task will be to tackle the problem with the lightest single-shot model available: SSD MobileNet v2 320×320, which appears to meet the criteria necessary, after analysing the TensorFlow 2.0 Detection Model Zoo. The architecture has been shown to recognise up to 90 classes and can be customised.

Training the model:

It’s time to think about the training process now that you’ve got a decent dataset and a strong model. The Object Detection API in TensorFlow 2.0 makes it simple to build, train, and deploy object detection models. I’m going to utilise this API and a Google Colaboratory Notebook to train the model.

Setting up the environment:

Select a GPU as the hardware accelerator in a new Google Colab notebook:

Change the type of runtime > Accelerator hardware: GPU

The TensorFlow Object Detection API can be cloned, installed, and tested as follows:

Then, using the appropriate commands, download and extract the dataset:

Setting up the training pipeline

I’m now ready to set up the training pipeline. The following instructions will be used to download pre-trained weights for the SSD MobileNet v2 320×320 on the COCO 2017 Dataset from TensorFlow 2.0:

The downloaded weights were pre-trained on the COCO 2017 Dataset, but as the goal is to train the model to recognise only one class, these weights will only be used to establish the network — this technique is known as transfer learning, and it’s widely used to speed up the learning process.

Finally, on the configuration file that will be utilised throughout the training, set up the hyper parameters. Choosing the best hyper parameters is a task that necessitates some trial and error.

I used a typical setup of MobileNetV2 parameters from the TensorFlow Models Config Repository and ran a series of tests on the SKU110K dataset to optimise the model for tightly packed scenes (thanks Google Developers for the free materials). Use the code below to download the configuration and verify the parameters.

To identify how well the training is going, I am using the loss value. Loss is a number indicating how bad the model’s prediction was on the training samples. If the model’s prediction is perfect, the loss is zero; otherwise, the loss is greater. The goal of training a model is to find a set of weights and biases that have low loss, on average, across all. The training process was monitored through Tensor board and took around 22h to finish on a 60GB machine using an NVIDIA Tesla P4.

Validate the model:

Now let’s evaluate the trained model using the test data:

The evaluation was done across 2740 images and provides three metrics based on the COCO detection evaluation metrics: precision, recall, and loss. The same metrics are available via Tensor board and can be analysed in an easier way. You can then explore all training and evaluation metrics.

Exporting the model:

It’s time to export the model now that the training has been validated. The training checkpoints will be converted to a protobuf (pb) file. This file is going to have the graph definition and the weights of the model.

As we’re going to deploy the model using TensorFlow.js and Google Colab has a maximum lifetime limit of 12 hours, let’s download the trained weights and save them locally. When running the command files. Download (“/content/saved_model.zip”), the Colab will prompt the file download automatically.

Deploying the model:

The model will be distributed in such a way that anyone with a web browser can open a PC or mobile camera and execute real-time inference. To do so, I’ll convert the stored model to TensorFlow.js layers format, load it into a JavaScript application, and make everything publicly available.

Converting the model:

Let’s start by setting up an isolated Python environment so that I may work in an empty workspace and avoid any library conflicts. Install virtualenv, then create and activate a new virtual environment in the inference-graph folder using a terminal:

venv source venv/bin/activate virtualenv -p python3

Install the TensorFlow.js converter by running pip install tensorflow.js tensorflow.js ten [wizard] install tensorflowjs

Start the conversion wizard: tensorflowjs_wizard

Now, the tool will guide you through the conversion, providing explanations for each choice you need to make. The image below shows all the choices that were made to convert the model. Most of them are the standard ones, but options like the shard sizes and compression can be changed according to your needs.

To enable the browser to cache the weights automatically, it’s recommended to split them.

Conclusion:

Apart from the precision, one of the most intriguing aspects of these tests is the inference time – everything is done in real time in the browser using JavaScript. In many consumers packaged goods industry applications, as well as other industries, SKU identification models that run in the browser, even offline, and use low computational resources are a necessary.

Enabling a Machine Learning solution to operate on the client side is a critical step in ensuring that models are used efficiently at the point of interaction with minimal latency and that problems are solved when they occur: right in the user’s hands.

Deep learning should not be expensive and should be utilised for more than just research, with JavaScript being ideal for production deployments. I hope you find this post useful.

Bioinformatics prospect after pandemic

Bioinformatics is a combination of more than two areas. It includes programming, data that are huge i.e. big data, biology, etc but especially it is a combination of biology and information technology. Whenever we are talking about data related to the medical field, means the generation of lots of data and the processing of those large data mathematically is known as bioinformatics. There is a huge opportunity in this field, as this field grows more in a situation like Covid. On average every day we are creating 1 terabyte of data through social networking sites. So again, this field includes more research in combination with big data. Experts in bioinformatics deal with analyzing the large sequences of gene data called genomics. In pharmaceutical, biochemical industries, biogas research plants, biotechnology, etc all these areas deal with genome sequencing. Now a day, gene sequencing is required to extract different features from one type of species to another. If the gene sequence is similar to the other species, then by doing some rearrangements in the sequence, even one species can be converted to other. That’s why this career includes a lot of scope such as industry, education, research and experiments for future investigation in medical. Information is very important aspect for analyzing human behavior. So, analysis of information in genomics field may lead your career towards the bioinformatics scientist, research scientist, microbiologist, agriculture scientist, molecular biologist, wild zoologist etc. Bioinformatics provide universally accessible database, from where several scientists extract the gene sequence data.

There are mainly three basic research areas in bioinformatics: Genetics or genomics, computational biology and system biology. As the name specifies, genetics or genomics mainly deals with the structuring or rearrangement of gene sequence or DNA strand. Whereas the field system biology includes functioning and evolution of molecules, species, tissues, cells, organism etc. by using the statistics and big data in information technology. This field works in the research of different drugs necessary for the evolution of new medicines. The last area i.e. bioinformatics and computational biology include mathematical computation of structuring of any genome sequence. It also includes statistical analysis or simulation of gene matching.

Apart from the above fields in bioinformatics, some specializations are also available, such as:

Computational genomics: This is a universal problem-solving specialization, deal with the human gene sequencing or we can say it is a study of analysis of genomic information related to humans. Research here include computational programing may be related to coding or non-coding. It also includes the networking and disease related problem solving to a particular field. If anyone is really interested for working in cancer genomics, then this is the area where one can research on disease causing cancer. There are various faculties working on the field from various countries. This is a diverse stream related to various sub domains also like networking, big data, statistics etc.

System immunology: It includes the mathematical modelling and various techniques in statistics and development of various methods related to bioinformatics. During Covid, this field actually related to evolution of new vaccine because this field works in the area of immune system. Immune system is a combination of thousands and thousands of cells and connected each other via molecular pathways. Various misfunctioning of human body parts or any sort of allergy or any infection related issues, are probably solved by finding vaccine to that issue. Immune cells are those cells that are the building blocks of the human, if immune system is week, means it can be affected at any point of time. So here those immune cells are extracted from a healthy human being and reinserted or replaced the damaged immune cells of other human body.

Artificial Intelligence & Machine learning technique: Machine learning technique is an efficient method for improved and better result in any field. When these techniques are applied to bioinformatics, where generation and processing of lots and lots of data are available, can’t be a better option than this. It includes deep learning process for the high-performance computing of rearrangement of data. This method is applied over the simulation field, where filtering and extraction of data from the thousands of long gene sequence.

DNA – RNA structure analysis: A, C, T, G are the four nucleotides works as a bases for DNA molecule. Adenine, cytosine, guanine and thymine form a double helix structure bond with hydrogen to form a DNA molecule. Gene is combination of different sequence of nucleotides, that combines together to form a chromosome. Nucleotides are the building blocks of nucleic acid such as hydrogen, carbon and oxygen. Chromosomes are the combination of various gene sequence, comprised together as a packed together to form DNA molecule. These ordered combinations of chromosomes are called genome. There are 4 types of chromosomes: metacentric, sub metacentric, acrocentric and telocentric chromosomes. Metacentric chromosomes have the centromere in the center, where the chromosomes are of equal length. Sub metacentric chromosomes have centromere slightly apart from the center. Here chromosome 4 and 12 are considered as sub metacentric. Acrocentric chromosomes have one strand very long and other is short. In human chromosome 13, 15, 21 and 22 are considered as acrocentric. Last one telocentric have the centromere at the end of the chromosome sequence. Telocentric chromosome is found in mice but not in humans and this becomes the research area where human gene sequence is found to be almost identical with the mice. For solving various diseases in humans, some features from mice are extracted.

So finally, I can say that there is a broad perspective both in research and job field. One can pursue this area to expertise their knowledge confined not only to a particular field but also to a combined interest in artificial intelligence, machine learning, big data, information technology, biology etc. Latest technology machine learning approach improves the research interest of researchers to this field. This are after pandemic have become more approachable and diversity field of research for the evolution of new vaccine or solution to the problems similar found in humans.

Sales and Marketing as a career, Post-Pandemic

Sales and Marketing activities span every business and every industry. Small businesses need salespeople, as do massive enterprise companies. Thus sales has always been a top career choice for graduates from every program, especially engineering and business graduates.

The sudden and emergence of the Covid 19 pandemic caused huge disruptions in the labour market worldwide, leading to millions being laid off and even losing their jobs. The rest tried their best to adapt work-from-home practices as physical premises had to be closed down. A few essential services, were however, kept open such as hospitals and logistics operations but with stifling protocols to contain the virus.

One of the major impacts of the pandemic was the emergence of remote working. McKinsey (The future of Work after Covid 19, February, 18^th, 2021) found that almost a fourth of the workforce in advanced countries could work out of their homes between 3-5 days a week. This was driven by the fact that several business activities especially those related to selling could easily be moved to the remote mode. This means that on the one hand, remote working may increase by a factor of four to five as estimated by McKinsey and this may result in reverse migration from cities to suburbs and large towns over a period of time.

Shifting the focus from service providers to consumers, the drastic shift to e-commerce and other online activities is likely to persist post-pandemic and McKinsey estimates (McKinsey Consumer Pulse Survey) the number to be almost 75%.

Closer home in India, the unemployment rate hit the roof with a record high of 27.1% in April, 2020 and numbers crossing 120 million (BBC Report). However, with receding pandemic and the resultant opening up of business across the country, a large portion of the economic activity is fast returning to pre-Covid levels.

With the cities and towns across India opening up rapidly, the greatest imperative for businesses today is acquiring revenue. In this scenario, most sectors have showed signs of an increase in hiring across sectors, especially in the sales and marketing segment.

The job aggregator, Naukri estimates that more than 23,000 Sales Business Development jobs are open in India currently out of which more than 1000 are new additions.

The major drivers for a strong uptick of Sales and Marketing positions are:

Revenue generation:

During the pandemic, almost all organisations have lost a substantial part of their business. It is imperative for them to ramp up revenues at the first available opportunity. Since Sales and Marketing roles have a direct and immediate impact on revenues, it is only normal that organisations will focus on building up their sales and marketing teams.

Brand awareness

India is a consumption-driven economy and with the return of pre-pandemic income levels, the vast Indian population, especially the burgeoning middle class is expected to engage in consumption with a vengeance. This is so because during the pandemic, consumers were home-bound and did not find enough avenues to channel their consumption. With the withdrawal of various restrictions post-pandemic and with the return of the pre-Covid income levels, Indian consumers will be looking forward to unique opportunities for indulging themselves. Many of the consumption habits have changed during pandemic, such as the shifting of several purchases to online modes, and this will result in a different pattern of consumer demand.

The role of the marketing function is critical in devising strategies for delivering these unique offerings customers and to devise innovative strategies for communication, engagement, and delivering customer satisfaction. This will lead to a spike in demand of marketing skillsets resulting in the demand for marketing professionals.

Changes in Sales and Marketing strategies:

The new business paradigm expected to emerge will witness major shifts in the way sales and marketing activities are conducted. Some of the trends which are expected to emerge are as follows:

Virtual sales techniques

Physical client visits are no longer seen as mandatory and both sales persons and customers have adapted well virtual means of interaction for buying and selling products and services. This trend will not only persist but may even accelerate in future as organisations attempt to expand their reach while keeping selling costs under control.

Change in technology

Technological changes are expanding at an ever increasing pace. This is causing unforeseen changes in the way business is conducted and no function, including sales and marketing, is safe from this. Already methods of customer engagement have undergone transformation through use of online platforms such as Zoom or MS Teams. Sales reporting has moved to online, and in many cases onto the cloud. Thus post-pandemic sales and marketing professionals will be expected to be techno savvy and ready to adopt new technologies.

Better use of social media

The pandemic forced people to be bound within the four walls of their homes. With the normal routes of social interaction cut off, they took to social media with a vengeance. The marketers and their business naturally followed their customers resulting in a sudden spurt in social media marketing practices. The post-Covid marketing professional cannot hope to succeed without a strong background in social media marketing techniques.

The future belongs to Hybrid

An adaptation to the pandemic, Hybrid sales is expected to be the most popular sales and customer engagement strategy in the initial stages of the sales process (The future of B2B sales is hybrid, April 27, 2022, McKinsey). Business-to-Business (B2B) sales, which is set to gain the most of it.

McKinsey found that Hybrid sales can be expected to increase revenue by more than half, prompting several large players in this market to shift to this strategy. Sales and marketing professionals should take note of it since organizations are creating new sales roles and recruiting appropriate talent to fill these vacancies.

It is thus clear that the landscape of the profession going forward will be very different from the one that we saw before the pandemic struck.