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About This Role
Job Description Summary
We are seeking a creative and curious individual to join our team as a Data Scientist. In this role, the successful candidate will lead the design, development and validation of advanced models aimed at improving decision\-making and supporting an evidence\-informed predictive framework.
The successful candidate will work closely with coaches, scouts, analysts and other stakeholders, translating complex data into key insights, developing interpretable and context\-relevant models across medical, sports sciences, scouting, squad planning and performance analysis, contributing to the development of proprietary models that provide Sporting Kansas City with a sustainable competitive advantage.
Sporting Kansas City is an equal opportunity employer. We celebrate diversity and equity and are committed to creating an inclusive environment for all associates. All associates are expected to positively collaborate with individuals of diverse backgrounds. We encourage all talented individuals looking for a challenge to apply.Job Description
People
- Work closely with coaches, analysts, scouts and other key stakeholders to identify football questions and translate them into advanced modelling projects.
- Develop strong relationships with the wider Data and Analytics team, ensuring alignment with Club and department strategy.
- Work very closely with the First Team Data Engineer to ensure advanced modelling is supported by reliable data and production\-ready workflows.
- Collaborate closely with the First Team Data Analyst, ensuring the model outputs are translated and communicated into clear and actionable insights.
- Ensure collaboration and communication across the wider Club as needed, championing the adoption of advanced analytics across Sporting Kansas City.
Process
- Lead the end\-to\-end development of applied advanced modelling projects, from problem definition with key stakeholders, through to deployment and review cycles.
- Develop robust tools for testing, validation, versioning, monitoring, model governance and documentation, focusing on creating and establishing best practices for experimentation.
- Continuously evaluate model performance, incorporating stakeholder feedback to ensure reliability and flexibility as the Club changes and evolves.
- Evaluate other emerging methods and research to ensure SKC stays current with data science best practice and trends.
Product
- Develop advanced models to support medical, sports sciences, scouting, coaching and performance analysis workflows.
- Support the development of advanced football metrics including player, team, league and valuation models, combining and utilizing multiple data sources.
- Develop advanced metrics, working extensively with event, tracking and physical data.
- Support the creation of predictive models related to load monitoring and management, player availability, injury risk and other key projects in collaboration with the medical and physical performance staff.
- In close alignment with the wider Data and Analytics teams, support the development of forecasting and scenario\-analysis tools, supporting squad and salary cap planning.
- Continuously challenge existing data, systems and practices to ensure development and drive innovation.
- Work closely with the First Team Data Engineer to productionize models and implement ML projects seamlessly.
Education \& Experience
- Bachelor's degree in computer science, data science or related STEM subject.
- Experience working in a Data Science, Machine Learning, Applied Statistics or similar role.
- Proven experience designing, validating and monitoring applied machine learning models.
- Robust experience using both SQL and Python for data analysis, modelling and automation.
- Strong understanding of statistical modelling, experimental design and model evaluation.
- Creative and curious problem solver with a positive attitude to new challenges.
- Proactive and keen to learn, able to pick up new skills and work as part of a team.
- Excellent attention to detail and evidence of working on developing strong analytical skills.
- Evidence of excellent communication skills, able to translate technical concepts into practical solutions.
Preferred Experience
- Prior experience working within an elite soccer club or high\-performance sporting environment.
- Experience working with soccer event, tracking and physical performance datasets.
- Previous experience developing models for elite athlete recruitment, physical performance, squad planning, player availability and load and fatigue management.
- Experience deploying machine learning models and working with cloud\-based environments.
- Strong understanding of technical and tactical aspects of soccer.
- Evidence of experience working in the MLS.
- Experience using data visualization tools such as Tableau or other equivalent software.
Physical Requirements
- Ability to work in office, stadium, and outdoor environments with the ability to travel as required.
- Ability to occasionally lift up to 25 pounds.
- Ability to work non\-traditional hours including evenings, weekends, and holidays.
Additional Responsibilities
- Represent Sporting Kansas City professionally at all times.
- Maintain confidentiality of sensitive information.
- Comply with Club policies and procedures.
- Perform other duties as assigned.
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 Sporting Kansas City, 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.
Sporting Kansas City AI Hiring
Sporting Kansas City has 1 open AI role right now. They're hiring across Data Scientist. Based in Center, TX, 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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