Data Scientist

• 2 to 5 years in data science or applied ML with a focus on product analytics, growth, or user behavior
• Strong SQL and real comfort working with large, event-level behavioral data at scale
• Solid classical ML foundations, including clustering (k-means, HDBSCAN, hierarchical), embeddings and vector similarity, dimensionality reduction (UMAP/PCA), and classification, with a working understanding of when each is and isn't the right tool
• Genuine skill at deriving insight from unstructured natural-language data (LLM traces, logs, tickets, free text) and turning it into predictive, queryable signal. Familiarity with topic-modeling and trace-clustering approaches (Clio-style summarize-then-embed pipelines, BERTopic, c-TF-IDF) is a strong plus
• Proficient in Python and the standard data-science stack (pandas, scikit-learn, statsmodels, numpy)
• Data engineering competence: you can design and ship ETL and data models (dbt or equivalent), not just query what already exists
• Experienced designing and analyzing experiments: sample sizing, power, significance, novelty effects, interference between tests, and causal methods
• You move fast and go deep, turning around in hours the analysis that takes most people days, because you've built the intuition to get to the right answer and the discipline to pressure-test it before anyone else sees it
• You dig past the top-line number to find the confound, you ask whether the metric measures what everyone assumes it measures, and you never hand over a figure without saying what it supports, what it doesn't, and what you'd check next
• You use AI agents aggressively to multiply your output, but you treat every AI-assisted result as a draft, not a deliverable
• Deep curiosity about user behavior and a real instinct for what drives growth, retention, and abuse
• Able to move fluidly between exploratory analysis, ML modeling, hypothesis testing, and crisp strategic recommendations, and to translate all of it into narratives that drive decisions

Nice to Have:

• Marketing Mix Modeling (MMM), media attribution, or incrementality and geo-testing experience, especially in low-tracking or post-cookie environments
• Experience at a PLG company with a self-serve funnel and freemium or usage-based/credit-based pricing
• Modern data stack (BigQuery, dbt) and product analytics platforms (PostHog, Amplitude, Mixpanel, Segment)
• Causal inference methods (difference-in-differences, synthetic control, propensity score matching)
• Fraud, trust and safety, or abuse analytics
• Working knowledge of embedding models and vector search, and the practical tradeoffs of running them at scale
• Familiarity with the economics of AI/LLM products, including COGS modeling where compute is the dominant variable cost
• You've built or contributed to AI-powered analytical tooling or novel measurement approaches

• Senior and Staff data scientists who want one of the hardest, most consequential analytics surfaces in AI software
• Applied ML practitioners who'd rather build the pipeline than wait for one
• Analysts who moved into ML and never stopped shipping
• Anyone drawn to where classical ML, LLM-native insight extraction, margin, attribution, and product all collide

• Daily Meals: Lunch and Dinner provided
• Family Insurance: 5 Lakhs worth of coverage for you and your family
• Unlimited Paid Time Off: Take the time you need to recharge and come back refreshed
• Flexible Working Hours: Work arrangements that fit your life and commitments

Let's build the future of software together.