Data Scientist

India·Bangaloremid

Data ScientistData

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Quick Summary

Key Responsibilities

Turn agent trajectories, support tickets, logs, and user prompts into structured,

Technical Tools

Data ScientistData

Emergent builds autonomous coding agents that replace traditional software development by generating, testing, and deploying production applications directly from plain-language intent. Our systems run in production at global scale and are used to build millions of real applications.

Since our public launch, we've crossed $130M in Annualised Revenue and grown to over 10M users across 190+ countries, who have built 12M+ applications on Emergent. We're backed by Creaegis, Khosla Ventures, SoftBank, Lightspeed, Together, Y Combinator, Google, Claypond and Sentinel Global.

We're solving the hard part of AI-driven software creation: correctness, reliability, security, and scale in real production systems. The team is built by repeat founders, Olympiad medalists, IIT & IIM alumni, and leaders from Google, Amazon, and Dropbox.

We're hiring builders who want ownership, speed, and impact at global scale.

Responsibilities

~2 min read

→Turn agent trajectories, support tickets, logs, and user prompts into structured, queryable signal through summarize-then-embed-then-cluster pipelines (à la Anthropic's Clio and Braintrust Topics): distill each trace along a dimension with an LLM, embed the summary, cluster and name the patterns, then classify at scale
→Surface early indicators of confusion, a coming bug wave, churn risk, or fraud that no dashboard would ever surface on its own, and route them to the right team
→Build predictive models that forecast conversion, retention, expansion, and churn, and embed those signals directly into product and growth workflows
→Own marketing attribution and MMM: build the media-mix and incrementality models that tell us what's actually driving signups and paid conversions when per-user attribution is partial and, on mobile, broken by design
→Own product and growth analytics across the self-serve funnel, web and mobile, covering activation, engagement, retention, and conversion, and design and analyze A/B and growth tests with real rigor around power, novelty effects, interference, and causal inference
→Run clustering pipelines over hundreds of thousands of agent trajectories to discover the recurring kinds of things users try to build and the recurring ways builds fail, then hand product a taxonomy nobody had to hand-label, along with which clusters predict churn
→Model the "aha moment" for new users, including text-derived features from their first prompts and first agent interactions, and rebuild onboarding around the earliest signals of long-term retention
→Build a gross-margin model that attributes LLM and compute cost down to the individual app and cohort, and tell product which segments are net-positive
→Untangle a fraud ring that looks anomalous on compute spend but has real payment history, decide whether it's an enforcement problem or a pricing problem, and defend the call with the data

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

~1 min read

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