TITLE:
Senior Data ScientistLocation: 201 3rd Street, Suite 500, San Francisco, CA 94103; Must be in the office 3 days per week and 2 days at home.Salary Range: 40 hours/week; $145,000 - $210,000/yearJob Description: Establishing scalable data foundations by designing and implementing robust data models, pipelines, and processes to support product and company-wide analytics at Harvey. Partnering with Product, Exec Team, GTM, & Finance to create and maintain standard dashboards that measure business health. Collaborating with cross-functional teams to understand their business needs and lead end-to-end analysis workflows that include data discovery and extraction (via SQL or APIs), analysis, ongoing scaled deliverables, and presentations. Delivering effective presentations of findings and recommendations to multiple levels of stakeholders. Collaborating closely with engineering to jointly lead data architecture decisions, co-design data schemas, and implement orchestration strategies that optimize pipeline reliability and data warehouse performance. Analyzing and synthesizing competitive dynamics and customer insights to uncover strategic growth opportunities, driving experimentation and continuous optimization across the business opportunities to improve our business. Must be in the office 3 days per week and 2 days at home.
Requirements: Bachelor's degree or foreign degree equivalent in Applied Math or Data Science, or related field and three (3) years of experience in the related role or job offered.
Experience and/or education must include:- Conducting data science and analytics work in legal technology, legal industry, or litigation consulting context
- Analyzing AI/LLM product performance with knowledge of retrieval-augmented generation (RAG), agentic workflows, prompt engineering, and model benchmark & evaluations
- Working closely with product, engineering, and design teams to define product metrics, diagnose metrics issues, and provide actionable business insights and decision support
- Utilizing SQL for complex data analysis including CTEs, window functions, JSON array operations, and query optimization techniques
- Building and maintaining data pipelines and transformation workflows using frameworks such as dbt or similar data engineering tools
- Building and maintaining analytics dashboards and data visualizations using tools such as Looker, Omni, and/or Streamlit
- Utilizing Python for data manipulation, regression, and machine learning analysis
- Designing and analyzing A/B tests and multivariate experiments to measure impact of product features on site performance.
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