Data Scientist III

Experteer Overview As a senior data scientist on the Revenue Science team, you own high-impact pricing and demand models end to end, from design through production and measurement. You will build scalable pipelines in imperfect infra, set technical standards, and mentor peers while tackling optimization and measurement challenges behind multi-million-dollar decisions. You drive stakeholder alignment with data-driven recommendations and measurable business impact, shaping pricing strategy and analytics at PODS. Compensation / Benefits * Own end-to-end models: design, deployment, monitoring for elasticity, demand, conversion, and forecasting; manage pipeline and stakeholder relationships * Formulate and solve pricing and capacity optimization problems using LP/QP/MIP with tools like Gurobi, CVXPY, OR-Tools * Establish model monitoring and drift detection; decide when to rebuild or retire models * Lead experimental design and causal measurement, including holdouts, geo/cluster randomization, power analysis, and DIF/ quasi-experimental methods * Build production-grade pipelines and data models in environments lacking mature infrastructure (Git, CI, orchestration, containers) * Scale analytics with distributed compute (PySpark/Databricks) and optimize SQL on large datasets * Create reusable assets (feature tables, model libraries, evaluation harnesses) to accelerate the team * Own senior stakeholder relationships; quantify business impact and explain measurement to leadership * Mentor earlier-career data scientists on methods, code, and judgment; review high-stakes analyses * Translate ambiguous commercial questions into tractable analytical plans Tasks * Expert Python (pandas/Polars, NumPy, scikit-learn, statsmodels) and advanced SQL * Strong foundation in GLMs, hierarchical models, and forecasting * Deep experience with causal inference and experiment design (holdouts, geo/cluster randomization, power analysis, DIF) * Proficiency in LP/QP/MIP optimization; experience with Gurobi, CVXPY, OR-Tools * Production deployment know-how: Git, CI, orchestration (Airflow/Databricks), containers, model monitoring * Experience with distributed compute (PySpark/Databricks) on Snowflake, Azure/Databricks, AWS/GCP * Familiarity with AI-accelerated workflows and ability to set standards for verification * Executive communication: translating analysis into confident, quantified recommendations under pressure * 7+ years post-PhD/masters experience building and deploying end-to-end models * End-to-end ownership of pipeline, model, deployment, and stakeholder engagement * Comfort with ambiguity and delivering impact before complete information * Pricing or logistics/capacity-constrained business experience is a plus Key requirements *

III power analysis, and DIF/ quasi-experimental methods * Build production-grade pipelines and data models in environments lacking mature infrastructure (Git, CI, orchestration, containers) * Scale analytics with distributed compute (PySpark/Databricks) and optimize SQL on large datasets * Create reusable assets (feature tables, model libraries, evaluation harnesses) to accelerate the team * Own senior stakeholder relationships; quantify business impact and explain measurement to leadership * Mentor earlier-career data scientists on methods, code, and judgment; review high-stakes analyses * Translate ambiguous commercial questions into tractable analytical plans Tasks * Expert Python (pandas/Polars, NumPy, scikit-learn, statsmodels) and advanced SQL * Strong foundation in GLMs, hierarchical models, and forecasting * Deep experience with causal inference and experiment design (holdouts, geo/cluster randomization, power analysis, DIF) * Proficiency in LP/QP/MIP optimization; experience with Gurobi, CVXPY, OR-Tools * Production deployment know-how: Git, CI, orchestration (Airflow/Databricks), containers, model monitoring * Experience with distributed compute (PySpark/Databricks) on Snowflake, Azure/Databricks, AWS/GCP * Familiarity with AI-accelerated workflows and ability to set standards for verification * Executive communication: translating analysis into confident, quantified recommendations under pressure * 7+ years post-PhD/masters experience building and deploying end-to-end models * End-to-end ownership of pipeline, model, deployment, and stakeholder engagement * Comfort with ambiguity and delivering impact before complete information * Pricing or logistics/capacity-constrained business experience is a plus Key requirements *