We do not ship models. We ship decisions that get made differently — forecasts a planner acts on, scores an operations team trusts enough to work from. If a model is accurate and nobody changes what they do, we have not delivered anything.
What you’ll own
- Forecasting, causal, and optimization work for clients in retail, CPG, BFSI, and manufacturing.
- The framing: turning a business question into something measurable before writing any modelling code.
- Deployment and monitoring alongside the platform engineers — your model runs in production, not in a notebook.
- The adoption case. You will sit with the people whose decisions change and prove the lift is real.
What we look for
- Strong Python and a solid grounding in causal inference, time series, or optimization — depth in one, literacy in the others.
- Experience with models that reached production and were measured after launch.
- The instinct to ask what decision this changes before asking which algorithm to use.
- Clear writing. Most of the persuasion happens on a page, not in a notebook.
Where you’ll sit
Mumbai or Indore, with time on the client floor as engagements require. Tell us which base suits you when you apply.