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About This Role
Job Title: Data Scientist, Baseball Analytics
Department: Baseball AnalyticsFLSA Status: Full-time, Exempt
Location: Baltimore, MD (Oriole Park at Camden Yards)
Job Summary
Data Scientists in Baseball Analytics build models and products that help the Baltimore Orioles acquire, develop, and deploy talent more effectively. This role exists to turn rigorous research into decisions that improve outcomes on the field, partnering with other departments and leadership in a collaborative, high-trust environment.
The Data Scientist will build high-quality predictive models and data pipelines, conduct and incorporate advantage-seeking research, and ensure information and tools are trusted and used. Success means delivering work that is accurate, well-tested, clearly communicated, and tied to organizational decision-making, with curiosity, humility, integrity, and a competitive drive to keep getting better. Primary ResponsibilitiesPrimary duties to include, but not limited to, the following:
- Clean, organize, and validate data from source databases.
- Build, improve, and maintain quantitative models using R or Python across a range of approaches, selecting the method appropriate to the task.
- Write clean, efficient, well-documented code following industry best practices; document methods, assumptions, and results; design and run appropriate tests to ensure accuracy and reliability.
- Partner with the development team to automate data pipelines and create usable tools for end users.
- Present findings clearly to technical and non-technical stakeholders; communicate proactively with leadership and teammates.
- Work with end users to gather feedback, understand modeling needs, and inform product development; raise constructive ideas for improvements to models and processes.
- Evaluate, recommend, and integrate new data sources.
- Effectively use modern AI tools and agents (e.g., coding assistants and agentic workflows) to improve productivity, while verifying outputs and applying sound analytical judgment.
Qualifications
Required:* Completed Bachelor's Degree in Data Science, Statistics, Analytics, Mathematics, Computer Science, or equivalent.
- Prior professional experience (1+ years) in data science or closely related role.
- Proficiency with R or Python.
- Experience with statistical analyses, data mining techniques, and working with large datasets.
- Experience building or maintaining data pipelines or partnering with developers to automate workflows.
- Deep passion for baseball and baseball analytics.
- Strong communication and presentation skills.
- Excellent interpersonal skills; collaborates well with teammates and cross-functional partners.
Desired:* Experience with database languages (e.g. SQL).
- Experience with machine learning, Bayesian methods, and probabilistic programming (e.g. Stan, PyMC).
DISCLAIMER: The statements herein are intended to describe the general nature and level of work being performed by the employee in this position. The duties listed do not represent an exhaustive list of all responsibilities, duties, and skills required of a person in this position.EQUAL OPPORTUNITY STATEMENT: The Baltimore Orioles are an Equal Opportunity Employer. It is the policy of the Baltimore Orioles to ensure equal employment opportunity without discrimination or harassment on the basis of race (including hair textures, afro hairstyles, or protective hairstyles), color, national origin or ancestry, religion or creed, gender or sex (including pregnancy), age, disability, citizenship status, marital status, veteran’s status, genetic predisposition or carrier status, gender identity, sexual orientation, or any other characteristic protected by law.
The anticipated base salary for this role is $90,000 annually depending on experience.
The Baltimore Orioles are committed to providing competitive pay and benefits for our employees.
The Baltimore Orioles provide generous benefits, including unlimited PTO, paid holidays, paid sick leave, health insurance (single and family), vision and dental coverage, life insurance, 401(k) plan with discretionary employer match, concession/merchandise discounts, free parking and complimentary baseball tickets to home games.
This information is being provided in accordance with the Maryland Equal Pay for Equal Work - Wage Range Transparency law.
Job Questions:
- This position is in-person. Are you located near Baltimore, MD? If not, will you relocate?
- Are you legally authorized to work in the U.S.?
- Do you currently, or will you in the future, require an employer to sponsor you for work authorization in the United States (for example, an H 1B, E 3, or TN visa)?
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 9,079 AI roles we're tracking, Data Scientist positions make up 4% of the market. At Baltimore Orioles, 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 $190,000 based on 1,503 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Baltimore Orioles AI Hiring
Baltimore Orioles has 1 open AI role right now. They're hiring across Data Scientist. Based in Baltimore, MD, US.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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