Reflecting and discovering the road ahead of 2024, on data science, its impact on job markets and career opportunities

Changes in the data science landscape and jobs market in India evolved over the last five years

The Indian job market has become very competitive?over the last?few years because of the demand for data science skill sets?and hiring the right talent. We observe a rapid change in the trend of hiring demand, from Data Analysts to big data experts to statisticians, mathematicians and data engineers who can deploy ML models in production and improvise them continuously. The noticeable?change in hiring talent?is mostly observed when the market also demands domain expertise in the specific area of the industry domain where data science needs to be applied. With data science, AI/ML getting applied across a plethora of industries today starting from Financial Services, Insurance, retail, travel and hospitality, healthcare and automobile industry, the market also looks for different types of AI/ML use cases a data science expert has worked in the relevant industry domain and the business value it has delivered, At the end companies try to prioritise the business value from the AI/ML models rolled out in production by the experts? - say for example a recommender system has brought in, and how the approach to the problem and solution can bring in value.

Deep dive into the key skills that are in demand for data science professionals in today's industry

Cloud - AWS, GCP or Azure with more focus on Machine?learning operations (LLMOps)

Data Science, AI/ML algorithms - Supervised and Unsupervised algorithms like Regression, Classification, Decision trees,? Clustering, Deep learning, NLP and Computer?Vision.

GENAI - ChatGPT, Prompt Engineering, LLM Fine Tuning, Retrieval-Augmented Generation (RAG)

Data Engineering - Big data skills like Hadoop, Apache Spark/Storm, Snowflake, Data ingestion and ETL pipelines?

Programming languages - Python, R

Emerging trends in data science that would have a significant impact in India over the next few years

The current market is still maturing in scaling data on different platforms on the Cloud and running advanced automation techniques to draw actionable insights that can drive businesses. The industry is turning more to hyper-automation to digitally transform large-scale legacy businesses to boost productivity and efficiency. Increasingly organizations are relying on integrating data from different sources, which has opened a new business area where data is being offered as a service (data exchange service) from one platform to another. IoT has gained a lot of popularity in recent years as industries are moving a lot of intelligent processing at the edge to increase processing time and delays. Due to the increased usage of edge devices, data privacy also has been a prime focus in the field of data science, in India. One of the most noteworthy topics of mention is "Responsible AI" which data science theory and practice have been spending a lot of time to democratise its usage, which is even supported by Nasscom and India.ai . Attention towards "Responsible AI" is aimed at regularising proper usage of data science and ML algorithms, by removing biased algorithms and by providing greater transparency to end-users and accountability to data science professionals. Among all, the most emerging trends in the field of data science have been Large Language Models (LLMs) and ChatGPT, which have been game changers in the last 12-18 months. This would make a significant impact on the Indian market as the industry would need more skilled people in areas to develop LLMs, LLM Operations (LLMOps), and Responsible usage of LLMs, across industry domains.

How AI and ML and data privacy are Influencing Data science job market

With the advent of data privacy and countries becoming stringent on regularizing AI, the job market also demands data privacy experts and AI ethics specialists. Data privacy experts should be able to drive all aspects of data and model privacy in the larger ecosystem of enterprises. In addition,? there is a demand for auditing AI solutions, where legal experts who understand the legal aspects of data privacy and fairness can certify the usage of AI solutions. Data science privacy experts should have a thorough understanding of all different types of adversarial attacks (white box or black box) and should be able to access, evaluate and suggest defense mechanisms against all attacks.

Advice to young professionals aspiring to enter the data science field in India

Young professionals should try to be full-stack data science professionals where they know holistically how an algorithm performs be it traditional ML, deep?learning or large language models. They can start with basics, and gain a deeper understanding of different models by participating in Kaggle and then aim to become end-to-end ML engineers.

As training and model prediction involve minimal steps with the use of already available Python?libraries, a true data science professional?must have a clear understanding of the end-to-end processes starting from data ingestion to feature engineering to model training, post-processing and prediction. In such a case, one must be ready to dig inside the Python libraries, study the implementation of research papers and must be ready to tweak it, based on the use case of the algorithm. Additionally, it becomes essential to be an expert to fine-tune the models more so when the model has achieved 85% accuracy. To achieve it a detailed knowledge of the implementation of the algorithm would help.