This position does not require an employee to be on-site full-time to perform most effectively. The employee's role enables them to work at a GM facility or off-site as frequently as needed or desired. This position requires an employee to be onsite 1-3 times per week , with flexible schedule for periodic race weekend remote support.
GM Motorsports Software Engineering is looking to expand its motorsports data science and analytics group. GM Motorsports is currently racing in NASCAR, IndyCar, IMSA and is currently pursuing an entry into Formula 1.
GM's Motorsports Software team analyzes, defines, and delivers next generation groundbreaking Motorsports IT software solutions. Using both innovative cloud- based infrastructure and software development standards, these solutions enable innovative interactions between GM Global Engineering, GM Motorsports, and our Race teams that accelerate our drivers to the finish line first! Our combined team of analysts, architects, developers, data engineers, testers, and project managers work with GM Motorsports Engineering and Race teams to ensure podium wins for GM's NASCAR, IndyCar, and IMSA sportscar teams!
In this Data Science role, you will have the opportunity to work hands-on with our full stack development team and data engineers developing solutions for strategic & analytic tools. Development activities include driving solution design, development, and deployment of analytic models in support of race team analytic groups. The team is responsible for both advanced analytics strategy and applied, project-based solutions, focusing on applying machine learning and artificial intelligence techniques to address specific needs of our data science, analytics, and GM key partner race teams. Additionally, you will drive development activities in accordance with appropriate methodologies to deliver tactical and strategic value each week to make a podium difference.
Work as a member of a cross-functional team to propose and implement high- impact data and analytic solutions that address business challenges across a variety of business units
Apply Data Science and Modeling techniques to drive data driven decision making and solve complex business problems:
Create breakthrough solutions, performing exploratory and targeted data analyses
Build predictive models and machine-learning algorithms
Analyze large amounts of diverse information to discover trends and patterns
Undertake preprocessing of structured and unstructured data
Monitor and sustain model effectiveness
Combine models through ensemble modeling
Present complex information using data visualization techniques
Work with diverse technical teams and provide data and analytic oversight to ensure project deliverables fulfill business needs and timing
At least 2 years of hands-on experience with scripting languages such as Python or R
2 or more years with object-oriented/object function scripting languages: Python, Java, C++, Scala, etc.
Degree in Mathematics, Statistics, Computer Science, Operations Research, Engineering, Economics, Data Science or another quantitative field
Ability to identify tasks which require automation and automate them
A demonstrable understanding of networking/distributed computing environment concepts
Machine Learning Libraries: TensorFlow, Scikit-Learn, Spark ML, or equivalent
Data Visualization: Tableau, Power BI or similar
Ability to prioritize and manage multiple tasks and projects at once without sacrificing quality
Highly collaborative work style with strong listening and communications skills
Ability to evaluate the big picture and solve business problems rather than focusing solely on metrics
Knowledge of Java, React, and SQL databases
Knowledge of Akka, Azure Cloud PAAS, Python, Kubernetes, Docker, GRPC
Big Data Tools: Hadoop, Spark, SQL, and NoSQL Database experience
Academic or work experience in a data-intensive field or industry
Experience with Motorsport Racing history/background a plus!
PhD. or Master's degree in Mathematics, Statistics, Computer Science, Operations Research, Engineering, Economics, Data Science or other quantitative field
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