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Sports Analytics Machine Learning Jobs in Dallas, TX

Vizient, Inc.

AVP, Machine Learning & Modeling

Irving, TX · On-site

$156K - $290K/yr

In this role, you will lead the design, development, and implementation of advanced analytics ... Strategic Leadership and Vision Provide strategic direction for the organization's machine learning ...

Vizient

AVP, Machine Learning & Modeling

Irving, TX

$156K - $290K/yr

In this role, you will lead the design, development, and implementation of advanced analytics ... Strategic Leadership and Vision Provide strategic direction for the organization's machine learning ...

SuperMom's LLC

Machine Learning Engineer II

Irving, TX · On-site

$120 - $180/hr

Process, analyze, and manipulate large structured and unstructured datasets to support analytics and machine learning initiatives. * Collaborate with data engineers, data scientists, product owners ...

7-ELEVEN

Machine Learning Engineer II

Irving, TX

Process, analyze, and manipulate large structured and unstructured datasets to support analytics and machine learning initiatives. * Collaborate with data engineers, data scientists, product owners ...

7-Eleven

Machine Learning Engineer II

Irving, TX · On-site

Process, analyze, and manipulate large structured and unstructured datasets to support analytics and machine learning initiatives. * Collaborate with data engineers, data scientists, product owners ...

Tiger Analytics

Machine Learning Engineer

Plano, TX · On-site

$120 - $150/hr

Overview Tiger Analytics is looking for experienced Machine Learning Engineer with Gen AI experience to join our fast-growing advanced analytics consulting firm. Our employees bring deep expertise in ...

Diamondback E&P LLC

Machine Learning Developer

Dallas, TX · On-site

$140 - $190/hr

The Machine Learning (ML) Developer is the first dedicated ML Development role in the department ... Strong interpersonal, analytical, and communication skills, with the ability to work effectively ...

Diamondbackenergy

Machine Learning Developer

Dallas, TX · On-site

The Machine Learning (ML) Developer is the first dedicated ML Development role in the department ... Strong interpersonal, analytical, and communication skills, with the ability to work effectively ...

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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.

What are popular job titles related to Sports Analytics Machine Learning jobs in Dallas, TX?

For Sports Analytics Machine Learning jobs in Dallas, TX, the most frequently searched job titles are:

What job categories do people searching Sports Analytics Machine Learning jobs in Dallas, TX look for?

The top searched job categories for Sports Analytics Machine Learning jobs in Dallas, TX are:

What cities near Dallas, TX are hiring for Sports Analytics Machine Learning jobs?

Cities near Dallas, TX with the most Sports Analytics Machine Learning job openings:

Sports Analytics Machine Learning jobs near you
Infographic showing various Sports Analytics Machine Learning job openings in Dallas, TX as of August 2026, with employment types broken down into 82% Full Time, and 18% Part Time. Highlights an 89% In-person, and 11% Hybrid job distribution.

Executive Director - Applied Artificial Intelligence Machine Learning

JPMorgan Chase & Co.

Plano, TX • On-site

$250 - $360/hr

Other

Re-posted 28 days ago

JPMorgan Chase & Co. rating

7.9

Company rating: 7.9 out of 10

Based on 500 frontline employees who took The Breakroom Quiz

78th of 175 rated banks

Job description

As an Applied AI/ML Executive Director within our dynamic team, you will apply your quantitative, data science, and analytical skills to complex problems. As a Machine Learning Director, you will have the opportunity to apply sophisticated machine learning methods to complex tasks including natural language processing, speech analytics, time series, reinforcement learning and recommendation systems. You will collaborate with various teams and actively participate in our knowledge sharing community. We are looking for someone who excels in a highly collaborative environment, working together with our business, technologists and control partners to deploy solutions into production. If you have a strong passion for machine learning and enjoy investing time towards learning, researching and experimenting with new innovations in the field, this role is for you.

Job responsibilities
  • Develop advanced agentic AI solutions involving structured and unstructed data, casual analytics, machine learning, deep learning, reinforcement learning, and optimization.
  • Design robust agent architectures combining LLM reasoning with tools, structured data, and APIs spanning state, memory, and context management, plus loop engineering (plan/act/observe, verification, termination, and fallback/escalation).
  • Engineer reliable agent-driven workflows emphasizing correctness, traceability, and control-aware behavior (guardrails, approvals, auditable decision paths).
  • Build knowledge-centric reasoning layers, including knowledge graphs and hybrid retrieval (RAG + graph + structured sources) to improve grounding and accuracy.
  • Drive specification-driven development: author specs and contracts (schemas, validators, tool/skill interfaces) and build evaluation/regression harnesses.
  • Advance agent quality via recursive self-improvement through automated evaluation and critique loops, red-team feedback, skill/prompt instruction optimization, and outcome-driven dataset curation (human-in-the-loop as needed).
  • Coach and mentor AI/ML team members, setting a high bar for engineering rigor and research depth.
Required qualifications, capabilities, and skills
  • PhD in a quantitative discipline, e.g. Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science Or with at least 5 years of industry experience or an MS with at least 7 years of industry or research experience in the field.
  • Extensive experience with machine learning and deep learning toolkits (e.g.: TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas)
  • Ability to design experiments and training frameworks, and to outline and evaluate intrinsic and extrinsic metrics for model performance aligned with business goals
  • Experience with big data and scalable model training and solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences.
  • Scientific thinking with the ability to invent and to work both independently and in highly collaborative team environments

  • Solid written and spoken communication to effectively communicate technical concepts and results to both technical and business audiences. Curious, hardworking and detail-oriented, and motivated by complex analytical problems

Preferred qualifications, capabilities , and skills:
  • Strong background in Mathematics and Statistics and familiarity with the financial services industries and continuous integration models and unit test development
  • Knowledge in search/ranking, Reinforcement Learning or Meta Learning
  • Experience with A/B experimentation and data/metric-driven product development, cloud-native deployment in a large scale distributed environment and ability to develop and debug production-quality code
  • Published research in areas of Machine Learning, Deep Learning or Reinforcement Learning at a major conference or journal
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