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Sports Analytics Machine Learning Jobs in Macomb, MI

Stellantis

Machine Learning Engineer

Auburn Hills, MI · On-site

$108K - $130K/yr

We are looking for a Machine Learning Engineer / Data Scientist to develop advanced statistical ... Perform exploratory data analysis and feature engineering on complex datasets * Collaborate closely ...

Stellantis

Machine Learning Engineer

Auburn Hills, MI · On-site

We are looking for a Machine Learning Engineer / Data Scientist to develop advanced statistical ... Perform exploratory data analysis and feature engineering on complex datasets * Collaborate closely ...

Stellantis

Machine Learning Engineer

Auburn Hills, MI · On-site

We are looking for a Machine Learning Engineer / Data Scientist to develop advanced statistical ... Perform exploratory data analysis and feature engineering on complex datasets * Collaborate closely ...

Varsity Tutors

Machine Learning Tutor

Detroit, MI · Remote

$18 - $40/hr

Emphasizes practical model development workflow and connects machine learning to recommendation systems, fraud detection, and predictive analytics. * Curriculum Awareness & Adaptive Instruction:

Stellantis

Machine Learning Engineer

Auburn Hills, MI

$108K - $130K/yr

We are looking for a Machine Learning Engineer / Data Scientist to develop advanced statistical ... Perform exploratory data analysis and feature engineering on complex datasets * Collaborate closely ...

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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 cities near Macomb, MI are hiring for Sports Analytics Machine Learning jobs? Cities near Macomb, MI with the most Sports Analytics Machine Learning job openings:
Sports Analytics Machine Learning jobs near you

Machine Learning Engineer

Stellantis

Auburn Hills, MI • On-site

$108K - $130K/yr

Full-time

Posted 11 days ago

Stellantis rating

7.5

Company rating: 7.5 out of 10

Based on 130 frontline employees who took The Breakroom Quiz

14th of 44 rated automakers

Job description

We are looking for a Machine Learning Engineer / Data Scientist to develop advanced statistical models and simulations that drive Vehicle Configuration Optimization (VCO). This role will leverage curated datasets to build a customer-level preference simulation engine, enabling optimized vehicle order guides (VOGs) for future model years.
This is a highly impactful role for an early-to-mid career data scientist who enjoys combining statistical rigor, large-scale data, and real-world business impact.
Key Responsibilities:
  • Build and run large-scale simulations (e.g., 50,000 synthetic customers) to model vehicle purchase behavior
  • Develop statistical and machine learning models using Databricks
  • Leverage datasets including:
    • Historical vehicle sales
    • Competitive sales data
    • Feature-level willingness-to-pay data
    • Customer preference models
  • Translate model outputs into optimized Vehicle Order Guides (VOGs) that inform product configuration decisions
  • Perform exploratory data analysis and feature engineering on complex datasets
  • Collaborate closely with Data Engineering to refine and leverage curated datasets
  • Communicate insights and model recommendations to business stakeholders
  • Continuously evaluate and improve model accuracy and assumptions

Basic Qualifications:
  • Bachelors Degree Required
  • Minimum 5 years of experience in data science, machine learning, or applied statistics
  • Strong experience with Databricks (critical requirement)
  • Proficiency in Python (Pandas, NumPy, scikit-learn, PySpark)
  • Strong SQL skills
  • Solid background in statistical modeling, simulation techniques, and experimental design
  • Experience translating analytical results into business decisions

Preferred Qualifications:
  • Experience with choice modeling, conjoint analysis, or demand modeling
  • Background in automotive, pricing, or product optimization analytics
  • Experience working with large-scale simulation frameworks
  • Familiarity with Spark and distributed computing
  • Exposure to MLOps or model productionization

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