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Strong VC/YC brand, popular Data Scientist title, mid-level experience and Bangalore location increase competition.
Requires LLM-native analytics, product-growth expertise, and AI economics, so background fit is highly domain-specific.
Explicit 2–5 year requirement plus mandatory ML, SQL, LLM, and data-engineering skills makes shortlisting highly strict.
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Own end-to-end data analytics and modeling for product growth, user behavior, and operational cost, turning raw agent trajectories and unstructured data into actionable insights.
Build predictive models for conversion, retention, churn, fraud detection, and marketing attribution including media-mix and incrementality in complex data environments.
Design, run, and analyze rigorous A/B tests and growth experiments with advanced methodologies including causal inference and novelty effects.
2 to 5 years of experience in data science or applied machine learning focused on product analytics or user behavior.
Strong proficiency in SQL and handling large-scale event-driven behavioral data.
Solid classical ML knowledge including clustering, embeddings, dimensionality reduction, and classification.
Proficient in Python with data science libraries (pandas, scikit-learn, numpy) and data engineering skills including ETL and data modeling (dbt or equivalent).
Experienced at building ML-driven analytics pipelines that extract insights from unstructured natural-language data using LLM-based summarize-then-embed-then-cluster techniques.
Skilled in marketing mix modeling, attribution, and advanced causal inference methods in product-led growth or freemium/usage-based SaaS environments.
Comfortable working at the intersection of product analytics, margin/cost modeling, fraud detection, and AI-powered measurement, with a high velocity and rigor in data analysis and strategic communication.