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About This Role
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 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.
ESSENTIAL DUTIES AND RESPONSIBILITIES
- Own models and analyses that inform pricing decisions:
o Independently estimate price elasticity at the corridor, segment, and channel level, selecting and defending the appropriate observational or experimental design.
o Develop and maintain conversion, demand, and forecasting models that account for price, mix, channel, and seasonality.
o 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:
o Design A/B tests end to end — power calculations, exposure rules, and metric definitions — with minimal oversight.
o Select and defend causal methods (difference\-in\-differences, synthetic control, regression discontinuity) when randomization is not feasible.
o Translate test results into clear recommendations with quantified uncertainty, including when the right answer is not to ship.
- Build durable analytical assets:
o Author well\-structured, reviewable Python using modern data tooling (pandas, scikit\-learn, statsmodels, or similar), with version control and code review as the default.
o Design and own key data models in Snowflake that other analysts and downstream tools rely on, including performance work on large tables.
o 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:
o Present results and recommendations to the Director of Pricing Strategy and Analytics, the broader Revenue Science team, and senior commercial stakeholders.
o Explain methodology and limitations in plain language for non\-technical stakeholders, and push back constructively when a request will not answer the real question.
o Provide informal mentorship and peer review to earlier\-career data scientists on methods, code, and communication.
MANAGEMENT \& SUPERVISORY RESPONSIBILITIES
- 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.
JOB QUALIFICATIONS: Essential Skills, Abilities and Example Behavior(s)
- 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.
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.
- 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.
- 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.
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At PODS, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
PODS AI Hiring
PODS has 2 open AI roles right now. They're hiring across Data Scientist. Based in Clearwater, FL, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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