GeoIQ (a Lenskart subsidiary) is India's leading hyperlocal location AI platform, helping businesses make precise, data-driven decisions using street-level intelligence across demographics, income, infrastructure, and commercial activity. As a Data Scientist, you will be a core individual contributor on a lean, high-output team — building and shipping classical ML predictive models for B2B clients across retail, fintech, and FMCG.
What You'll Do
Build and ship new predictive models and driver-factor analyses every week (60–70% of your time)
Maintain, monitor, and measure the real-world impact of deployed models (30–40% of your time)
Work independently from problem framing through to a base model — handholding is minimal and limited to client-facing presentation
Collaborate with cross-functional teams to translate business problems into ML solutions
Report directly to the Hiring Manager; contribute as a fully autonomous individual contributor from day one
Must-Have Requirements
Python (DS-grade) — Pandas, ML libraries, and visualisation packages; backend Python does not qualify
Classical ML model building — regression, classification, XGBoost, boosting/bagging techniques, Scikit-learn, StatsModels
Hands-on predictive modelling ownership — ability to scope and build a base model end-to-end independently
Strong problem-solving and live coding ability under time pressure
SQL — mandatory for working across client data pipelines
Minimum 2 years of full-time Data Science experience (internships do not count unless from a premium company)
Good to Have
Experience at product companies — Ola, Uber, Swiggy, Zomato, or similar high-scale consumer tech
Analytics consulting background — Mu Sigma, ZS Associates, Fractal, Deloitte, HSBC, Capgemini, or Genpact
Tier 1 / Tier 2 college — BSc/MSc in Maths, Statistics, Economics, or M.Tech (preferred, not mandatory if at a strong company)
What We're Not Looking For
Profiles where 60–70%+ of experience is in GenAI, LLMs, RAG systems, or applied AI with no classical ML grounding
Python used exclusively for backend/software engineering — not data science
Serial job-hoppers (switching every year across 4–5 companies) without valid reasons such as mass layoffs or restructuring
Candidates with low join intent or likely to offer-shop
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