Senior Scientist

Job Description

The Role

Our 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 open workspaces and virtual connection platforms to inspire productivity and flexible collaboration. We are proud to support our employees’ volunteer interests and prioritize efforts that give back to our communities.

The Applied Analytics & Insights Team is seeking applications from highly motivated and qualified individuals for the position of Senior Scientist. This is a unique opportunity to join a multi-disciplinary team of experienced individuals who will be driving the design, development, and deployment of advanced statistical and mathematical solutions running the gamut across the technological spectrum. 

The Applied Analytics & Insights team is focused on execution-based analytics to help GM organizations achieve better business outcomes through contextualized solutions with targeted actions. The team is responsible for both advanced analytics strategy and applied, project-based solutions, focusing on the company’s most critical business areas.

Candidates will develop solutions to problems across the enterprise using AI/ML and other analytical techniques. In some cases, invention and generation of intellectual property for General Motors will be required. Candidates will work as a member of a multi-disciplinary team of various experience levels that frame opportunities, wrangle the necessary data, and design and execute analytical models in support of better business decisions.

What You'll Do

As a Senior Scientist, the chosen candidate will be expected to provide technical thought leadership for their team as problems are explored, selected, and worked against. Senior Scientists are expected to be leaders on their teams: mentoring junior team members, providing guidance to stakeholders, and diving into the technical details of the work as necessary. 

As a Senior Scientist, it is expected that you have deep analytic and scientific knowledge of your chosen field and are capable of applying that knowledge across domains to create business value and impact. The ‘How’ this is done can vary wildly from scientist to scientist. Some achieve this by becoming experts at acquiring new business domains. Others are experts in artificial intelligence and machine learning who’ve developed significant abstraction skills. Still others may bring in unique skills around multidisciplinary design optimization or topological data analysis. In all cases though, the successful candidate will need to be able to apply what they bring in quickly and pick up their new domains as they go along.

• Analyze datasets to extract actionable insights and patterns that drive business decisions.
• Develop and implement machine learning models to solve complex business problems and improve processes.
• Clean, preprocess, and transform raw data into usable formats, ensuring data quality and integrity.
• Explore and experiment with new data sources, techniques, and algorithms to continuously improve analytical capabilities.
• Collaborate with cross-functional teams to identify data-driven opportunities and provide guidance on data collection and storage.
• Able to work independently and guide more junior resources through standard projects

Required Qualifications

Candidates must be collaborative team players who will work closely with professionals across the enterprise including other scientists, software developers, analysts, product leads, and potentially vehicle engineers. The candidate should have 3+ years of experience among a broad set of potential roles, including but not limited to:

• Data Scientist
• Research Scientist
• AI Analyst
• Developer
• Architect
• ML Engineer
   

Additional desired technical expertise:

• Analysis and modeling of telematics data, especially from a physical perspective
• Experience with commercial datasets
• Manipulating high-volume, high-dimensionality data from varying sources to highlight patterns, anomalies, relationships, and trends with machine learning techniques
• Modeling data abstractions employing multiple non-linear mathematical and statistical modeling approaches
• Applying machine learning methodology, with emphasis on applying and integrating machine learning with traditional analytic methods
   

Development experience in one or more of the following areas:

• Python proficiency required
• CPU frameworks for machine learning in python – numpy, pandas, scikit-learn
• GPU fram