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Sports Analytics Machine Learning Jobs in Alabama

If you are passionate about helping customers solve complex big data challenges, leveraging Databricks for advanced analytics, machine learning, and AI-driven insights, and enjoy working in a ...

Data, Analytics & AI Engineer

Birmingham, AL ยท On-site

$107K - $128K/yr

... Machine Learning solutions. โ€ข Build intelligent workflows that automate multi-step business ... time analytics. โ€ข Champion responsible AI adoption across the organization. โ€ข Identify ...

Data, Analytics & AI Engineer

Birmingham, AL

$107K - $128K/yr

Data, Analytics & AI Engineer Help Build the Future of Data, Analytics & AI Are you passionate ... Drive AI Innovation Design, prototype, and deploy AI and Machine Learning solutions. Build ...

The ideal candidate has hands-on experience with data wrangling, exploratory data analysis, statistical modeling, machine learning algorithms, and data visualization , and can work effectively with ...

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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 Alabama? For Sports Analytics Machine Learning jobs in Alabama, the most frequently searched job titles are:
What cities in Alabama are hiring for Sports Analytics Machine Learning jobs? Cities in Alabama with the most Sports Analytics Machine Learning job openings:
Mid-Level AI / Machine Learning Software Engineer

Mid-Level AI / Machine Learning Software Engineer

Modern Technology Solutions, Inc.

Huntsville, AL โ€ข On-site

$112K - $135K/yr

Full-time

Re-posted 21 days ago


Job description

We are seeking a Mid-Level AI / Machine Learning Software Engineer to support development of scalable data analysis and machine learning capabilities across large datasets and real-time data streams. The role focuses on designing, implementing, and optimizing machine learning models and data pipelines using Python and modern deep learning frameworks.
The ideal candidate has strong programming fundamentals, hands-on model development experience, and is comfortable working with large structured and unstructured datasets in production environments.
Primary Responsibilities
  • Design, develop, and maintain Python-based data processing and analytics solutions
  • Implement and optimize machine learning and deep learning models
  • Work with large datasets and streaming data sources
  • Develop reusable data structures and efficient algorithms for analysis workflows
  • Build and evaluate models for classification, prediction, and pattern recognition
  • Integrate AI/ML capabilities into software systems and pipelines
  • Collaborate with software engineers, data engineers, and analysts to deploy solutions
  • Perform model validation, performance tuning, and debugging
  • Document architecture, implementation, and usage of developed tools

Required Qualifications
  • 3+ years of professional software development experience
  • Strong Python development skills
  • Experience working with large datasets and/or streaming data
  • Proficiency in machine learning and deep learning frameworks:
  • PyTorch
  • TensorFlow
  • Keras
  • Hugging Face Transformers
  • Understanding of machine learning concepts and model architectures, including:
  • Decision Trees / Random Forests
  • LSTM / sequence models
  • Experience implementing, training, and evaluating ML models
  • Knowledge of data structures, algorithms, and performance optimization
  • Familiarity with version control (Git) and collaborative development workflows

Desired / Preferred Qualifications
  • Experience with Retrieval-Augmented Generation (RAG)
  • Experience with Model Context Protocols (MCP) or similar agent/tool interaction frameworks
  • Experience with GPU acceleration and CUDA architecture
  • Drivers, runtime, and APIs
  • Experience with deep learning and reinforcement learning libraries
  • Experience building or consuming real-time data pipelines
  • Data visualization and exploratory analysis (Matplotlib, Seaborn, Plotly, etc.)
  • Familiarity with model deployment and inference optimization
  • Experience working in containerized or distributed environments

Education
  • Bachelor's degree (or working toward a degree) in Computer Science, Data Science, Engineering, Mathematics, or related field
  • (Equivalent practical experience considered)

Nice-to-Know Technologies
  • Linux development environments
  • Jupyter notebooks
  • Docker or container basics
  • Basic command line usage

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