Data Scientist

Compensation: ₹5L – ₹10L • No equity

This role is not about building a model in isolation. It's about taking a client's vague business problem, making it measurable, and turning it into a system their people rely on to make a decision. When that happens, you can point to the decision that changed because of your work. Few roles let you see your impact that clearly.

You own the problem end to end: framing it, engineering the features, modeling it, proving it moved the number that matters, and getting it into a form the client actually uses. Predictive analytics, decision intelligence, and the systems that put insight where decisions are made. That's your territory.

The work:

• Shape a client's real challenge into a problem we can solve
• Engineer features and build models tied to outcomes that matter: productivity, better decisions, lower cost
• Design the experiments and metrics that show a value slice delivered
• Build the decision-intelligence and BI deliverables (PowerBI, Tableau) that surface insight at the point of decision
• Explain your reasoning, and your trade-offs, to client stakeholders who don't work in math

Where your work ends. You consume the data the Data Engineer serves; you don't build the pipelines. You own the model and the insight; when a model needs to run as a hardened production or agentic system, you hand it to the AI Engineer, and you rely on the Software Engineer for any custom application interface. Your ownership is the modeling and the decision it drives.

What success looks like:

• A client decision measurably improves, and you can prove which number moved
• Models are validated and honest about their uncertainty
• Insight lands where decisions are actually made, not in a report no one opens
• Every value slice you deliver carries a metric that shows it worked

The stack we work in today: Python with Scikit-Learn and PyTorch; NLP and computer vision where the problem calls for it; Neo4j Graph Data Science for graph-based analytics; and PowerBI or Tableau for decision support. Beyond the tools, you should be able to package, deploy, and monitor your own work, because a model that never ships helps no one, and communicate clearly with client stakeholders. In a consulting setting that communication often matters more than the modeling.

About The Strong AI, and how we work

The Strong AI is an end-to-end AI implementation partner. Clients come to us because most organizations can run an AI experiment, but few can turn it into a system their business depends on. We close that gap. We don't hand over slideware or a notebook; we build systems that work inside a client's business, and where they want it, we run them.

You'll work across engagements and industries, on different problems and often different stacks. We're technology-agnostic: the problem and the client's environment choose the tools, so treat any stack we list as the ground we work on today, not a gate.

Across all roles, we ask for the same way of working:

• Real software. Tested, reviewed, versioned code the next person, or the client's team, can pick up.
• MLOps mindset. A model's life starts at deployment. Monitoring, retraining, drift, and rollback are handled before anything breaks.
• Systems thinking. You see both the value slice and the whole it compounds into.
• Quality and security, owned by you. Designed in from the first decision, not inspected in at the end. Everyone builds to the highest standard.
• Built for handover. Clear code and docs the client's own team can understand, operate, and take over.