Hybrid:
This role is categorized as hybrid. This means the successful candidate is expected to report to the Milford Proving Grounds location three times per week, at minimum [or other frequency dictated by the business].
Relocation:
This job is not eligible for relocation benefits. Any relocation costs would be the responsibility of the selected candidate.
Why work for us
There’s never a more exciting time to work for General Motors! We are very passionate about our bold vision of a world with Zero Crashes, Zero Emissions and Zero Congestion. Our transformed culture is focused on building inclusive teams, where differences and unique perspectives are embraced so you can contribute to your fullest potential as you pursue your career. Our locations feature a variety of work environments, including techy open workspaces and virtual connection platforms to inspire productivity and flexible collaboration. And we are proud to support employee volunteer interests and make it a priority to join together in efforts that give back to our communities.
The Role:
We are at a pivotal time in our industry where simulation techniques are paving the way towards a digital revolution of our engineering tools and systems. As we transform and drive new software content for our vehicles, our vision is one that strongly augments simulation techniques with Artificial Intelligence (AI). This will enable maximum efficiency, increased robustness, optimized workflows, all scalable.
Towards this goal, we are creating dedicated teams to focus on AI, data analytics, simulation, and automation frameworks for hardware, software and system design and development.
This role is key and is specifically critical to embedding advanced data analytics in our models that will be key to robust AI and optimization implementations.
Key Responsibilities / what you’ll do:
- Use data analytics and signal processing to analyze simulation output data using techniques like wavelet transforms and motif discovery (or other)
- Develop custom feature extraction methods for predictive modeling then used in optimizations.
- Apply statistical methods, anomaly detection, and clustering to uncover patterns.
- Work with large scale data sets and collaborate with experts in our team to incorporate physical interpretations of insights
- Create interactive data Visualizations to communicate and interpret
- Help build ML models that may be used as surrogates in simulations
- Use your expertise to prototype new data driven solutions that fit our goals as they evolve and we future proof our technology stacks.
- Challenge the status quo continuously, with a main aim being to further our understanding of our data.
- Master ambiguity in a way that can leverage creative insights while remaining grounded in your deliverables.
- Lead and mentor others in the team towards a common goal
Qualifications and Required Skills:
- Expert understanding of data science, advanced statistics, signal processing and simulation frameworks
- In depth knowledge of the core programming languages (Python, JavaScript, C/C++, etc.).
- Core data tools knowledge: Python (NumPy, Pandas, SciPy), SQL, signal processing libraries (PyWavelets, Tsfresh) and more.
- Knowledge of ML modeling and models and its modern toolsets (e.g. Scikit-learn, XGBoost for classification/regression tasks)
- Willingness to learn and continue developing knowledge in an up-and-coming field.
- Excellent problem-solving skills with the ability to thrive in a demanding, fast-paced work environment.
- Strong interpersonal and communication skills and a willingness to collaborate cross-functionally with different teams.
Education & Experience:
- Bachelor’s degree in mathematics, Engineering, Physics, Computer Science, or a related field with a focus on Data Science.
- 5+ years in signal processing, feature extraction, or data analysis.
What Will Give You a Competitive Edge (Preferred Qualifications):
- Master’s degree or PhD in mathematics, Engineering, Physics, Computer Science, or a related field with a focus on Data Science.
- Experience with Automotive SW development process
- Knowledge of Full-Stack AI Deployment (e.g. scalable ML pipelines (MLOps) using Docker, Kubernetes, FastAPI, cloud services, or other modern toolsets)
- Experience with advanced simulation or CoSimulation frameworks
- Knowledge of robust optimization techniques (GA’s, PSO, MDO, etc.)
