Principal Data Scientist & Engineer

 

G-P helps organizations build global teams in minutes, not months. As part of this mission, we’ve created an indispensable AI agent for HR leaders, G-P Gia™ .

Gia is our AI-powered global HR agent that provides HR compliance guidance instantly. Built on over a decade of global employment and legal expertise and 100,000+ vetted articles, Gia analyzes and generates compliant documents and delivers the answers that HR leaders trust — reducing reliance on outside legal counsel and cutting compliance costs by up to 95%.

About The Position:

We’re building GIA — AI for General Counsel, in-house legal teams, and HR teams. GIA is a startup that demands extreme ownership, relentless execution, and zero tolerance for waiting on someone else to make the call. This role sits at the intersection of data science, data engineering, and product analytics. You build the pipelines, run the analysis, train the models, and — most importantly — tell us what the data means for the product. Not dashboards for the sake of dashboards. Insights that change what we build next.

What You Will Do:

• Build and own data infrastructure — pipelines, warehousing, ETL/ELT, data quality; make sure the foundation is solid
• Analyze product usage and user behavior — identify patterns, segment users, surface what matters from the noise; think like a product person, not just a data person
• Build models that ship — LLM-based systems, traditional ML (classification, clustering, NLP), evaluation frameworks; whatever the problem needs
• Define and track the metrics that matter — activation, retention, engagement, PQLs; connect data to product and GTM decisions
• Run experiments and measure impact — A/B tests, causal analysis, cohort studies; rigorous but fast
• Turn data into product conviction — you don’t just hand off charts, you tell the team what to do and why

What We Are Looking For:  

Minimum Requirements: 

• 7+ years across data science, data engineering, and analytics — you do all three, not just one
• Strong SQL and Python — complex queries, data modeling, scripting, analysis; this is your daily toolkit
• Databricks or equivalent modern data platform experience (Snowflake, BigQuery)
• LLM experience — fine-tuning, prompt engineering, embeddings, RAG, evaluation; not just API calls
• Traditional ML depth — classification, regression, clustering, NLP, feature engineering; you pick the right tool for the problem
• Product mindset — you filter signal from noise, understand user behavior, and connect analysis to product decisions
• Pipeline engineering — you build reliable, scalable data pipelines, not notebooks that break in production
• Clear communicator — you present findings to non-technical stakeholders with clarity and conviction

Preferred Qualifications: 

• Experience at an early-stage startup or as a founding data hire
• Built product analytics from scratch — instrumentation, event taxonomy, dashboards, self-serve reporting
• Legal or HR domain experience
• Experience with LLM evaluation and observability (tracing, scoring, drift detection)
• Familiar with dbt, Airflow/Dagster, Spark, or similar orchestration and transformation tools

How we evaluate

• Give you a real dataset and ask what you’d do with it — we want product thinking, not just technical chops
• Walk us through a time your analysis changed a product decision
• Design a data pipeline or model architecture on the whiteboard
• Show us how you’d instrument and measure a new feature from scratch

Interested? Reach out with an example of an insight you uncovered that changed what a team built.