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Sports Analytics Machine Learning Jobs (NOW HIRING)

Givzey

Machine Learning Engineer

Design, develop, and implement machine learning models and algorithms ... Analyze large datasets and extract meaningful insights * Collaborate with cross-functional teams to ...

Rapsodo

General Application (US)

Saint Louis, MO · On-site

Rapsodo Inc. is a sports analytics company that uses computer vision and machine learning to help all athletes maximize their performance. Our proprietary technology applications range from helping ...

Full Scope

Machine Learning Engineer

Reston, VA

Conduct data analysis and preprocessing to ensure high-quality data for model training. * Optimize and fine-tune models for performance, accuracy, and scalability. * Deploy machine learning models ...

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Sports Analytics Machine Learning information

What is sports analytics machine learning?

Sports analytics machine learning is the application of data science and machine learning techniques to analyze sports data, such as player statistics, game outcomes, and biometric information. Professionals in this field develop models to identify patterns, predict player performance, optimize team strategies, and gain competitive advantages. This work involves collecting large datasets, cleaning and processing data, and using algorithms to extract actionable insights that can benefit teams, coaches, and athletes. Sports analytics with machine learning is increasingly used in professional sports to inform decisions about training, recruitment, and game tactics.

How do Sports Analytics Machine Learning professionals typically collaborate with coaches and athletes to impact game strategy?

Sports Analytics Machine Learning professionals often work closely with coaches and athletes by translating complex data insights into practical recommendations. They attend strategy meetings, present findings through visualizations, and help interpret trends that can influence training, player selection, and in-game tactics. Effective communication is key, as these professionals must bridge the gap between technical analyses and real-world sports applications. This collaborative environment not only enhances team performance but also provides opportunities to see the direct impact of your work on the field.

What are the key skills and qualifications needed to thrive as a Sports Analytics Machine Learning Specialist, and why are they important?

To thrive as a Sports Analytics Machine Learning Specialist, you need a strong background in statistics, data analysis, programming (typically in Python or R), and an understanding of machine learning algorithms, often supported by a degree in data science, statistics, or a related field. Familiarity with data visualization tools, sports databases, and machine learning frameworks like TensorFlow or scikit-learn is essential, along with experience using SQL and data pipelines. Strong problem-solving, communication, and collaboration skills help translate complex data findings into actionable insights for coaches, players, and stakeholders. These skills are crucial for extracting meaningful patterns from vast sports datasets and driving performance improvements or strategic decisions within sports organizations.
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Infographic showing various Sports Analytics Machine Learning job openings in the United States as of June 2026, with employment types broken down into 100% Full Time. Highlights an 83% In-person, and 17% Remote job distribution.
Systems Architect - Data Science & Advanced Analytics

Systems Architect - Data Science & Advanced Analytics

Analytica

Bethesda, MD • On-site

$68 - $87.50/hr

Full-time

Posted 18 days ago

Job description

Analytica is seeking a Systems Architect - Data Science & Advanced Analytics to provide strategic and technical leadership in designing, implementing, and modernizing enterprise data and advanced analytics solutions. This role serves as the lead architect for data science initiatives, responsible for developing scalable analytics environments, machine learning solutions, and enterprise data architectures with a strong emphasis on Databricks, Apache Spark, and modern cloud-based data platforms.
The ideal candidate will work closely with data scientists, data engineers, business stakeholders, and technical leadership to develop innovative analytics solutions that transform complex data into actionable insights. This individual will provide technical vision, establish best practices, and lead the adoption of advanced analytics and machine learning capabilities across the organization.
Key Responsibilities
Data Science Leadership
  • Serve as the Lead Architect for enterprise Data Science and Advanced Analytics initiatives.
  • Develop and drive the organization's vision and strategy for Big Data, Artificial Intelligence, and Machine Learning solutions.
  • Lead the design and implementation of scalable data science environments that support experimentation, model development, validation, and deployment.
  • Provide technical leadership and mentorship to Data Scientists, Machine Learning Engineers, and Analytics teams.
  • Identify emerging technologies and analytical methodologies that improve organizational capabilities and mission outcomes.
Advanced Analytics & Machine Learning
  • Architect end-to-end machine learning and advanced analytics solutions using modern data platforms and distributed computing frameworks.
  • Design scalable workflows for data preparation, feature engineering, model training, evaluation, and production deployment.
  • Collaborate with cross-functional teams to translate business and mission objectives into advanced analytical solutions.
  • Guide the development of predictive models, statistical analyses, optimization models, and AI-driven decision support tools.
  • Establish best practices for model governance, reproducibility, documentation, and performance monitoring.
Data Architecture & Big Data Solutions
  • Design enterprise data architectures that enable large-scale analytics and machine learning workloads.
  • Lead the development of data pipelines and analytical frameworks utilizing Databricks Lakehouse architecture, Delta Lake, and Apache Spark.
  • Ensure data solutions are scalable, reliable, high-performing, and aligned with enterprise architecture standards.
  • Architect integrated data ecosystems that support structured, semi-structured, and unstructured data sources.
  • Lead modernization initiatives that enhance data accessibility, analytical capabilities, and operational efficiency.
Strategic Collaboration
  • Partner with executive leadership and business stakeholders to define data and analytics roadmaps.
  • Translate complex technical concepts into business-focused recommendations and strategic initiatives.
  • Lead architecture reviews and provide expert guidance on enterprise analytics solutions.
  • Foster collaboration between Data Science, Data Engineering, and business teams to deliver high-impact analytical products.

Required Qualifications
Experience
  • Bachelor's degree in Computer Science, Data Science, Statistics, Engineering, Mathematics, or a related technical discipline (Master's preferred).
  • 10+ years of progressive experience designing and implementing advanced analytics, machine learning, or Big Data solutions.
  • Demonstrated experience serving as a technical lead or subject matter expert for enterprise data science initiatives.
  • Experience leading technical teams and architecting large-scale analytics platforms.
  • Experience architecting enterprise-scale data science and advanced analytics platforms.
  • Experience developing AI/ML solutions supporting mission-critical or large-scale business operations.
  • Knowledge of modern data governance and responsible AI principles.
  • Experience with experimentation frameworks, model operationalization, and analytical product development.
  • Familiarity with Agile product development and collaborative data science workflows.
Required Technical Skills
Data Science & Machine Learning
  • Machine Learning model development and lifecycle management
  • Statistical analysis and predictive modeling
  • Artificial Intelligence and Advanced Analytics methodologies
  • Feature engineering and model evaluation techniques
  • Data exploration and analytical solution design
Big Data & Data Engineering
  • Databricks Lakehouse Platform
  • Apache Spark
  • Delta Lake
  • Distributed data processing frameworks
  • ETL/ELT and modern data pipeline architectures
  • Data modeling and enterprise data architecture
Analytics & Visualization
  • Advanced analytics solution design
  • Business Intelligence and data visualization concepts
  • Dashboard and reporting architecture
  • Data storytelling and insight generation
Leadership
  • Technical strategy development
  • Enterprise architecture governance
  • Cross-functional team leadership
  • Stakeholder engagement and executive communication
  • Mentoring and coaching technical teams
Certifications:
Candidates should possess one or more of the following certifications:
Databricks
  • Databricks Certified Professional Data Scientist
  • Databricks Certified Professional Data Engineer
  • Databricks Certified Machine Learning Associate
  • Databricks Certified Developer for Apache Spark
Big Data & Analytics
  • Apache Spark or Big Data certifications
  • Tableau Certified Professional (Desktop or Server)
  • Other industry-recognized data science, analytics, or visualization certifications

Analytica LLC is an Equal Opportunity Employer. We are committed to providing equal employment opportunities to all individuals, regardless of race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, or any other characteristic protected by applicable federal, state, or local law. As a federal contractor, we comply with the Vietnam Era Veterans' Readjustment Assistance Act (VEVRAA) and take affirmative action to employ and advance in employment qualified protected veterans. We ensure that all employment decisions are based on merit, qualifications, and business needs. We prohibit discrimination and harassment of any kind. Analytica LLC also provides reasonable accommodations to applicants and employees with disabilities, in accordance with applicable law.
To enhance efficiency, fairness, and accuracy, Analytica may use AI-assisted tools to support certain aspects of our hiring process.
  • Application Review: AI tools may help identify skills and experiences relevant to the role.
  • Interview Support: AI-powered notetaking tools may be used during interviews to document discussions and summarize key points.

These tools are used to assist our team. All hiring decisions are made by Analytica recruiters and hiring managers.
By submitting an application, you acknowledge that AI-assisted tools may be used to support parts of the application and interview process.
When receiving email communication from Analytica, please ensure that the email domain is analytica.net to verify its authenticity.

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