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From Development Work to Artificial Intelligence: Abhisek Barik’s Unconventional Journey Into Data Science
How a career that began in the development sector gradually evolved into work across data science, predictive analytics and artificial intelligence
From Development Work to Artificial Intelligence: Abhisek Barik’s Unconventional Journey Into Data Science
There is a familiar story in the technology industry: a young professional enters engineering, moves into software, learns data science and eventually finds a place in artificial intelligence.
Abhisek Barik’s journey has taken a different route.
There was no obvious sign at the beginning of Abhisek Barik’s career that artificial intelligence would eventually become its defining direction.
His professional journey began in the development and social-impact sector, where the questions were often deeply human: How can limited resources reach the people who need them? How can programmes be measured? And how can better information lead to better decisions?
Years later, those same questions have followed him into a very different world—one shaped by data, machine learning and artificial intelligence.
The transition was gradual rather than planned as a single career leap. Barik moved from development work into data analytics, then predictive modelling and machine learning, progressively building the technical foundation that now informs his work in AI.
His early experience with organisations including ChildFund International and Save the Children gave him exposure to large-scale social and development challenges. Working with data in these environments meant dealing with complexity that could not always be reduced to a clean dataset or a simple model.
That perspective became useful as he moved into consulting and industry roles with Deloitte, Genpact and Grant Thornton.
Across financial services, healthcare, education, agriculture, FMCG, e-commerce and public-sector programmes, his work has increasingly involved using analytics to understand problems and translate findings into decisions.
An important part of that work has been communication. Barik has engaged with CXO-level executives and senior government stakeholders, including Secretaries, Joint Secretaries and senior Class I officers. In such settings, technical accuracy is only one part of the job; complex analysis has to become understandable enough to influence strategy.
Technically, his experience spans classification, regression, ensemble modelling, time-series analysis, NLP, deep learning and predictive analytics.
More recently, his interests have moved toward newer forms of AI. He has experimented with Generative AI, vector databases, Retrieval-Augmented Generation (RAG), sentiment analysis and AI-powered intelligence platforms.
There has also been an entrepreneurial dimension to that exploration. One project involved a Graph Neural Network-based financial fraud detection solution deployed on AWS, alongside experimentation with SaaS-based AI products and MVP development.
His interests are not confined to commercial applications. Climate intelligence has emerged as another area of exploration, particularly the use of predictive modelling to examine future carbon-footprint scenarios and emerging sustainability challenges.
Alongside his professional work, Barik is pursuing an MS in Applied Data Science from the University of San Diego, extending an academic path that already includes postgraduate learning in management, data science and analytics.
What makes his career interesting is not simply the number of technologies he has worked with. It is the connection between the different stages.
The development sector gave him exposure to human and societal problems. Consulting introduced him to complex organisational environments. Data science provided the analytical language. And AI is now giving him another set of tools to approach problems that are becoming increasingly difficult to solve through conventional methods alone.
His evolution, therefore, is less a story of leaving one field behind and more a story of accumulation.
From development programmes to boardroom discussions, from predictive models to emerging AI systems, Barik’s career reflects a broader change taking place across the technology landscape: the most consequential data and AI work increasingly sits at the intersection of technology, business and real-world problems.
