Data Scientist II

PODS is building the analytical infrastructure to understand customer behavior, quantify price elasticity, and inform daily commercial decisions across our long-distance and local moving businesses. As a Data Scientist 2 on the Revenue Science team, you’ll report to the Director of Pricing Strategy and Analytics and own analytical projects end to end — framing the commercial question, choosing the method, building the model, and delivering the recommendation.

You’ll work in Snowflake, Python, and experiment design with substantial independence, partner directly with pricing analysts and product managers, and help raise the technical bar for the team, producing analyses that feed pricing decisions worth millions of dollars to the business.

• Own models and analyses that inform pricing decisions:
  • Independently estimate price elasticity at the corridor, segment, and channel level, selecting and defending the appropriate observational or experimental design.
  • Develop and maintain conversion, demand, and forecasting models that account for price, mix, channel, and seasonality.
  • Quantify the impact of pricing actions on conversion, container utilization, and lifetime revenue, and translate results into terms commercial leadership can act on.
• Lead experiment design and causal measurement:
  • Design A/B tests end to end — power calculations, exposure rules, and metric definitions — with minimal oversight.
  • Select and defend causal methods (difference-in-differences, synthetic control, regression discontinuity) when randomization is not feasible.
  • Translate test results into clear recommendations with quantified uncertainty, including when the right answer is not to ship.
• Build durable analytical assets:
  • Author well‑structured, reviewable Python using modern data tooling (pandas, scikit‑learn, stats models, or similar), with version control and code review as the default.
  • Design and own key data models in Snowflake that other analysts and downstream tools rely on, including performance work on large tables.
  • Build dashboards and reports that surface model outputs in a form operational users can act on, and automate recurring analyses so they run without manual effort.
• Communicate and mentor:
  • Present results and recommendations to the Director of Pricing Strategy and Analytics, the broader Revenue Science team, and senior commercial stakeholders.
  • Explain methodology and limitations in plain language for non‑technical stakeholders, and push back constructively when a request will not answer the real question.
  • Provide informal mentorship and peer review to earlier‑career data scientists on methods, code, and communication.

• This role does not have direct reports and reports to the Director of Pricing Strategy and Analytics. Provides informal mentorship and peer review to earlier‑career data scientists.
• Other duties as assigned.

• Statistical modeling depth:
  Strong command of regression, generalized linear models, hierarchical models, and applied ML techniques, with the judgment to select — and defend — the right specification for the question.
• Applied causal inference:
  Working fluency in multiple quasi‑experimental techniques (difference-in-differences, synthetic control, instrumental variables, regression discontinuity) and the judgment to match method to question independently.
• Experiment design and analysis:
  Ability to lead A/B tests end to end — power calculations, exposure rules, metric definitions, and interpretation — with minimal oversight.
• Advanced SQL and Python:
  Performance‑conscious SQL on a modern cloud data warehouse (Snowflake preferred), including work on large tables, and well‑structured, reviewable Python (pandas, scikit‑learn, stats models, or equivalent stack).
• Production‑minded workflow:
  Fluency with git and code review, and a track record of making analyses reproducible and automating recurring work; exposure to orchestration tooling (Airflow, Databricks, or similar) is a plus.
• AI‑accelerated analytical workflows:
  Fluent, default use of AI tools (Claude, Cursor,…