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Remote role, metro context, mid-level experience requirement, and popular data science title raise competition.
ML/LLM and product analytics skills are transferable, but legal/HR domain preference increases domain specificity.
Explicit 5+ years and mandatory LLM, Databricks, SQL/Python, and pipeline engineering imply strict filters.
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Build and own data infrastructure including pipelines, warehousing, ETL/ELT, and data quality for reliable analytics.
Analyze product usage and user behavior to generate actionable insights that influence product and GTM decisions.
Develop and deploy machine learning models including LLM-based and traditional ML methods; define and track key product metrics and run experiments to measure impact.
5+ years experience spanning data science, data engineering, and analytics.
Proficient in SQL and Python for complex queries, scripting, and analysis.
Experience with modern data platforms such as Databricks, Snowflake, or BigQuery.
Hands-on expertise with LLM techniques including fine-tuning, prompt engineering, embeddings, and retrieval augmented generation (RAG).
Operates at the intersection of data science, engineering, and product analytics with strong product intuition and ability to connect data insights to product outcomes.
Experienced in building and scaling production-grade data pipelines and analytics systems, preferably in early-stage startup environments.
Demonstrates clear communication skills for explaining technical analyses to non-technical stakeholders and driving product conviction.