Work with large datasets. Own efficient querying, cleaning, labeling, and taxonomy alignment for brands, SKUs, and categories.
Design sampling and classification strategies that turn noisy LLM outputs and crawler logs into reliable brand and product insights.
Use LLMs and NLP to extract structure from unstructured text at scale. Topics include query fan-out, sentiment, citation extraction, and entity linking for brands, products, and creators.
Define product-grade metrics. Create durable definitions for visibility score, answer coverage, product presence, and agentic checkout readiness.
Build and run experimentation frameworks. A/B tests, holdouts, counterfactuals, and uplift modeling to quantify impact on citations, share of voice, and conversions.
Develop and refine predictive models that analyze and forecast AI search behavior across models and surfaces.
Translate complex findings into clear decisions. Partner with the founding team to inform roadmap, pricing, and customer playbooks.
Create evaluation harnesses. Establish automatic evals and human-in-the-loop labeling for model quality, bias, and drift across LLM providers.
Detect anomalies. Build monitors for crawler behavior, rankings, and feed health to catch regressions before customers do.

3 to 7 years in applied analytics or data science within tech, marketing, or ads. Startup or high-growth experience preferred.
Strong Python and SQL. Comfortable in notebooks and in code reviews.
Skilled with sampling and inference. Stratified sampling, bootstrapping, extrapolation, reweighting, and variance estimation.
Solid ML toolkit. Time series, classification, regression, weak supervision, and methods to estimate event frequency from partial observations.
Practical LLM knowledge. Strengths in prompt design, structured extraction, embeddings, and an understanding of model limits and failure modes.
Curious and current on multi-modal and LLM research. You enjoy reading papers and pressure testing ideas in real data.
Builder mindset in a fast team. You value clarity, speed, and ownership.
Nice to have:
Experience with large-scale information extraction or search quality
Background in causal inference, MMM, or attribution models
Hands-on work with product feeds and retail catalogs
Contributions to open source or published work we can read
Deployed side projects we can click through.

Data Scientist

Key skills