Data Scientist II

PODS•Clearwater, FL

15h

About The Position

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.

Requirements

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, statsmodels, 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, Copilot, or similar) across code, query, and documentation work, with sound judgment about when output requires verification and a track record of helping teammates adopt the patterns that work.
Clear communication: Ability to present methodology and results in plain language to senior and non-technical stakeholders, both in writing and in person, and to defend analytical choices under questioning.
Bachelor’s degree in a quantitative field (Statistics, Economics, Operations Research, Computer Science, Engineering, Mathematics, or similar) required; Master’s preferred.
5+ years of applied data science or quantitative analytics experience, with hands-on work on pricing, demand, conversion, marketing, or revenue problems.
Track record of owning analytical projects end to end — from question framing through modeling to a recommendation stakeholders acted on — with measurable business impact.

Nice To Haves

Experience deploying or automating analytical work (scheduled pipelines, orchestrated jobs, or production models) is a plus, as is experience with applied Bayesian methods or optimization.
Experience in moving, logistics, e-commerce, travel/hospitality, or other capacity-constrained consumer businesses is a plus.

Responsibilities

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, statsmodels, 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.