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About This Role
JOB SUMMARY
As a Data Scientist on the Operations Data Science \& AI team, you will report to the Director, Operations Data Science \& AI and work with senior data scientists and operational stakeholders to develop optimization models, predictive models, and automated workflows. Your work will help PODS make better decisions across capacity planning, routing, scheduling, resource allocation, and other field operations.
ESSENTIAL DUTIES AND RESPONSIBILITIES
- Develop optimization solutions:
+ Build and support optimization models for capacity planning, routing, scheduling, and resource allocation.
+ Formulate business problems using decision variables, objectives, and operational constraints.
+ Assist in root\-cause analysis to surface optimization and automation opportunities across field operations.
- Develop predictive models:
+ Build, test, and maintain forecasting, regression, classification, and anomaly\-detection models for operational problems.
+ Prepare and validate data, engineer features, and evaluate model results.
- Build and automate workflows:
+ Build reproducible data pipelines and automate recurring analyses, model runs, and reporting, replacing manual processes.
+ Contribute to shared tooling, frameworks, and standards so that solutions are repeatable.
- Develop analytical assets and data models:
+ Maintain data models in Snowflake that other analysts and downstream tools rely on.
+ Create dashboards and decision\-support tools that make results actionable.
- Document and communicate clearly:
+ Document logic, methodology, and assumptions alongside every model, tool, or pipeline you build.
+ Present findings and their limitations in plain language to the team and operational stakeholders.
JOB QUALIFICATIONS: Essential Skills, Abilities and Example Behavior(s)
- Mathematical optimization: Hands\-on experience formulating and solving mixed\-integer linear programming models, including defining decision variables, objectives, and constraints.
- Optimization tools: Previous experience with Gurobi, Pyomo, OR\-Tools, PuLP, CPLEX, or a similar optimization library or solver is required.
- SQL and Python fluency: Strong SQL on a modern cloud data warehouse, preferably Snowflake, and Python for analysis and model development.
- Applied machine learning: Experience building, testing, and validating forecasting, regression, classification, or other predictive models, with judgment about which method fits the problem.
- Workflow automation: Experience building reproducible data pipelines and automating recurring analyses and model workflows.
- Data visualization: Ability to communicate analytical and model outputs through clear visualizations and practical decision\-support tools.
- Communication and documentation: Ability to explain methods and results clearly and document work so that it is reproducible and reviewable.
- Structure amid ambiguity: Ability to turn loosely defined operational problems into clear analytical questions and practical solutions.
JOB QUALIFICATIONS: Education \& Experience Requirements
- Bachelor’s degree in a quantitative field such as Data Science, Statistics, Operations Research, Industrial Engineering, Applied Mathematics, Physics, Computer Science, Engineering, Economics, or a related field required; master’s degree preferred
- 5\+ years of applied data science, machine learning, or quantitative analytics experience. Relevant internship, co\-op, or graduate research may count toward experience.
- Hands\-on experience with SQL on a modern cloud data warehouse (Snowflake preferred) and with Python for analysis.
- Experience or coursework in machine learning and mathematical optimization, with exposure to cloud\-based data platforms such as Snowflake or AWS.
- Experience supporting an Operations, Supply Chain, logistics, or other capacity\-constrained business is a plus.
PHYSICAL REQUIREMENTS
- Ability to sit at a desk and use a computer for up to 8 hours a day; Ability to use hands and fingers to type on a keyboard and use a mouse to navigate; Vision sufficient to view small details on a computer monitor
- Ability to stand and walk up to 8 hours a day; ability to stoop, bend and lift boxes weighing up to 50 lbs.
- Ability to hear and verbally communicate using a telephone handset and/or connected headset device
WORKING CONDITIONS
- Regular business hours. Some additional hours may be required.
- Travel requirements: Negligible
- Climate\-controlled office environment during normal business hours.
- Regular attendance and punctuality required
- May be subject to pre\-employment criminal background check and/or drug screening as well as random drug screenings in accordance with company policy
Equal Opportunity Employer
This employer is required to notify all applicants of their rights pursuant to federal employment laws. For further information, please review the Know Your Rights (https://www.eeoc.gov/poster) notice from the Department of Labor.
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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