Data Scientist I

PODS; Clearwater (FL); On-site; USD 110,000 - 160,000; Full time

Job summary

PODS is seeking a Data Scientist to join the Revenue Science team. You will work with senior data scientists, pricing analysts, and product managers to turn commercial questions into rigorous, testable answers.

Expect to work in Snowflake, Python, and experiment design to drive pricing decisions worth millions. You will build models to quantify price elasticity, conversion, and demand, design A/B tests, and translate results into clear recommendations with quantified uncertainty.

Qualifications

• Bachelor's in a quantitative field required; Master’s preferred.
• 3+ years in applied data science or quantitative analytics focusing on pricing, demand, conversion, or revenue.
• Hands-on SQL on a modern cloud data warehouse (Snowflake preferred) and Python for analysis.
• Experience with multiple causal inference techniques or Bayesian methods is a plus.
• Experience in moving, logistics, e-commerce, travel/hospitality, or capacity-constrained consumer businesses is a plus.

Responsibilities

• Build models and analyses to inform pricing decisions.
• Estimate elasticity at corridor/segment/channel levels using observational and experimental data.
• Develop conversion and demand models considering price, mix, channel, and seasonality.
• Quantify pricing actions impact on conversion, container utilization, and lifetime revenue.
• Design and analyze experiments, including A/B test design and power calculations.
• Run causal analyses when randomization is not feasible and translate results into actionable recommendations.
• Create reproducible analytical assets in Python and Snowflake.
• Build dashboards and reports surfacing model outputs for operational users.
• Present findings clearly to stakeholders and document analyses for reproducibility.

Skills

Statistical modeling

Causal inference

Experiment design

SQL & Python

AI-accelerated workflows

Clear communication

Education

Bachelor's degree in quantitative field

Master's preferred

Tools

Snowflake

Python

Pandas

scikit-learn

statsmodels

Job description

Job Summary

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 on the Revenue Science team, you'll report to the Director of Pricing Strategy and Analytics and partner with senior data scientists, pricing analysts, and product managers to turn commercial questions into rigorous, testable answers. You'll spend your time in Snowflake, Python, and experiment design, producing analyses that feed pricing decisions worth millions of dollars to the business.

Essential Duties And Responsibilities

• Build models and analyses that inform pricing decisions:
• Estimate price elasticity at the corridor, segment, and channel level using observational and experimental data.
• Develop conversion and demand models that account for price, mix, channel, and seasonality.
• Quantify the impact of pricing actions on conversion, container utilization, and lifetime revenue.
• Design and analyze experiments:
• Partner with senior data scientists and pricing analysts on A/B test design, including power calculations, exposure rules, and metric definitions.
• Run causal analyses (difference-in-differences, synthetic control, regression discontinuity) when randomization is not feasible.
• Translate test results into clear recommendations with quantified uncertainty.
• Build reproducible analytical assets:
• Author analyses in Python using modern data tooling (pandas, scikit-learn, statsmodels, or similar).
• Develop and maintain key data models in Snowflake that other analysts and downstream tools rely on.
• Build dashboards and reports that surface model outputs in a form operational users can act on.
• Communicate findings clearly:
• Present results to the Director of Pricing Strategy and Analytics and the broader Revenue Science team.
• Explain methodology and limitations in plain language for non-technical stakeholders.
• Document analyses so that conclusions are reproducible and reviewable by peers.

Management & Supervisory Responsibilities

• This role does not have direct reports and reports to the Director of Pricing Strategy and Analytics.
• Other duties as assigned.

JOB QUALIFICATIONS: Essential Skills, Abilities and Example Behavior(s)

• Statistical modeling fluency: Solid grounding in regression, generalized linear models, and applied ML techniques, with good judgment about which method fits which question.
• Applied causal inference: Working knowledge of one or more quasi-experimental techniques (difference-in-differences, synthetic control, instrumental variables, or regression discontinuity), with the ability to apply an appropriate method to a given question.
• Experiment design and analysis: Hands-on experience with A/B testing, including power calculations, metric definitions, and interpreting results.
• SQL and Python fluency: Production-quality SQL on a modern cloud data warehouse (Snowflake preferred) and Python (pandas, scikit-learn, statsmodels, or equivalent stack).
• AI-accelerated analytical workflows: Demonstrated use of AI tools (Claude, Cursor, Copilot, or similar) to accelerate code, query, and documentation work, with judgment about when AI output requires verification.
• Clear communication: Ability to explain methodology and results in plain language to non-technical stakeholders, both in writing and in person.

JOB QUALIFICATIONS: Education & Experience Requirements

• Bachelor's degree in a quantitative field (Statistics, Economics, Operations Research, Computer Science, Engineering, Mathematics, or similar) required; Master's preferred.
• 3+ years of applied data science or quantitative analytics experience, with hands-on work on pricing, demand, conversion, marketing, or revenue problems.
• Hands-on experience with SQL on a modern cloud data warehouse (Snowflake preferred) and with Python for analysis.
• Experience with multiple causal inference techniques or with applied Bayesian methods is a plus.
• Experience in moving, logistics, e-commerce, travel/hospitality, or other capacity-constrained consumer businesses is a plus.