Senior Data Scientist, Engineer

🕒 April 20

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G-P

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

👥 HR Tech

📋 Compliance

☁️ SaaS

HR Tech • Compliance • SaaS

G-P is a leading global company specializing in Employer of Record (EOR) solutions, providing businesses with the tools to hire, onboard, and manage teams in over 180 countries quickly and compliantly. The company offers a comprehensive suite of global employment products under its G-P Meridian Suite™, including services for hiring employees and contractors, managing global payroll, time management, expenses, HR support, and employee assistance. G-P is recognized for its compliance expertise, featuring the world's first AI-based HR compliance advisor, G-P Gia™, which delivers AI-driven advice and solutions for HR compliance challenges. With a strong network of partners and industry-leading EOR SaaS solutions, G-P supports HR, legal, and financial compliance globally. Their offerings are designed to simplify the employment lifecycle and accelerate global business expansion.

📋 Description

• 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

🎯 Requirements

• 5+ 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.

🏖️ Benefits

• generous paid parental leave • flexible time off • spending accounts • medical insurance • dental insurance • vision insurance • sabbatical after 5 years and more.