Data Scientist

Role Overview

• Partner with Product and Engineering to identify high-impact opportunities, frame ambiguous problems, define success metrics, and choose pragmatic approaches (heuristics, statistics, ML, or GenAI).
• Lead rigorous experimentation across teams: hypothesis design, metric/guardrail definition, power analysis, A/B testing (or quasi-experiments), and clear readouts that drive decisions.
• Build and iterate on ML/AI capabilities that ship to production (e.g., classification, information extraction, ranking/recommendations, and GenAI components such as RAG or developing the agent harnesses for our core agentic journeys), optimizing for value-added, latency, and cost.
• Establish best-in-class evaluation practices for both ML and LLM features: golden datasets, offline/online evaluation plans, regression suites, and monitoring that catches quality drift early.
• Enable engineers to build safely and effectively with AI by coaching on prompt patterns, tool/function calling, structured outputs, guardrails, and debugging/evaluation workflows.
• Design and support agentic workflows where they add real product value, with clear constraints, observability, and fallbacks.
• Support the end-to-end lifecycle of deployed models and AI systems: data requirements, training/fine-tuning where relevant, validation, deployment, monitoring, incident response, and continuous improvement.
• Raise org-wide leverage by creating reusable assets (evaluation harnesses, shared datasets, templates, documentation) and running enablement workshops.
• Communicate insights and tradeoffs clearly to technical and non-technical stakeholders, turning analyses into decisions and measurable impact.
• Champion responsible, privacy-aware AI: appropriate data handling, bias/fairness considerations where applicable, and human-in-the-loop workflows when needed.

Requirements

• 3+ years (or equivalent) delivering data science work that shipped to production and/or materially influenced product direction
• Experience collaborating cross-functionally and communicating clearly with diverse stakeholders; ability to influence without authority
• Strong Python and SQL skills, with the ability to write maintainable, production-quality code (testing, reviews, documentation)
• Demonstrated mentorship/enablement—helping other engineers and teams adopt best practices and ship faster with higher quality
• At least 3 of:
• Strong applied statistics and experimentation skills (A/B testing, causal thinking, metric design, interpretation under uncertainty)
• Proven ability to evaluate and improve models in real conditions: dataset design, error analysis, offline metrics, online measurement, monitoring, and iteration
• Hands-on experience building with LLMs in product contexts, including some of: RAG/grounding, tool/function calling, structured outputs, prompt iteration, quality/cost/latency tradeoffs
• Practical approach to LLM evaluation: golden sets, regression testing, human review loops, and monitoring for quality drift
• Experience with modern MLOps/LLMOps practices (experiment tracking, ETL pipelines, versioning, CI/CD for ML, observability)
• NLP and information extraction/classification on noisy social/content data
• Experience developing and evaluating large scale Retrieval, Recommendation
• and Search Systems.

Tech Stack

• ETL
• Python
• SQL

Benefits

• Competitive Salary
• Remote Work Options with Hybrid Flexibility and Home Office Set-Up Stipend
• Coworking Office Subscription for Collaborative Spaces
• Health, Dental, and Life Insurance Coverage*
• Open Vacation Policy and Flexible Holiday Schedule to Suit Your Needs
• Paid Parental Leave to Support Quality Time with Your Loved Ones
• Career Development, including Internal and External Training Opportunities