AI/ML Data Scientist (GPSSC)

General MotorsWarren, MI
1dHybrid

About The Position

At General Motors, our product teams are redefining mobility. Through a human-centered design process, we create vehicles and experiences that are designed not just to be seen, but to be felt. We’re turning today’s impossible into tomorrow’s standard —from breakthrough hardware and battery systems to intuitive design, intelligent software, and next-generation safety and entertainment features. Every day, our products move millions of people as we aim to make driving safer, smarter, and more connected, shaping the future of transportation on a global scale.. The Role Our Product Safety Data Analytics team is seeking an experienced AI/ML Data Scientist with hands-on experience in the full end to end data science lifecycle for artificial intelligence & machine learning. This role requires programming and statistical techniques to solve complex problems and work within a team framework on new AI/ML solutions. If you're passionate about driving innovation through AI/ML and have experienced delivering solutions to your customers, this role offers an exciting opportunity to contribute to impactful projects in a dynamic team environment. As an AI/ML Data Scientist, you will be responsible for working with our customers to understand their challenges and needs, develop new machine learning solutions, work with ML Ops in a cloud environment, develop proof of concepts for new generative AI solutions and Agentic AI solutions.

Requirements

  • Bachelor's degree in Computer Science, Engineering, Mathematics, or a related field
  • 5+ years of experience in machine learning, engineering, data science, or a related field
  • Looking for experience in the following:
  • Programming & Frameworks: Python, R, Java, PySpark, PyTorch, TensorFlow, Scikit-learn, LangChain, SQL
  • Machine Learning & AI: Large Language Models (LLMs), Generative AI, RAG, Generative AI, Deep learning, Reinforcement Learning, Natural Language Processing (NLP), SVM, XGBoost, Random Forest, Decision Trees, Clustering
  • Data Engineering: Databricks, Hadoop, SQL, Data Pipelines, Data Preprocessing & Feature Engineering
  • Cloud & Big Data Platforms: (Preferred Microsoft Azure (Data Lake, Machine Learning, Databricks), Nice to Have (AWS (S3, SageMaker, Bedrock) or Google Cloud Platform (BigQuery, Dataflow, AI Platform) )
  • Deployment & MLOps: MLflow, Model Monitoring & Versioning, Docker & Kubernetes, GitHub, Jira
  • Data Analysis & Visualization: Tableau, PowerBI, Pandas, NumPy
  • Proven track record delivering AI/ML solutions to customers that get continued usage and drive value
  • Excellent communication and collaboration skills, with the ability to work effectively in a team environment
  • Strong problem-solving mindset and a proactive attitude towards learning and self-improvement

Nice To Haves

  • Deep knowledge of GM’s Data Ecosystem
  • Deep knowledge of GM’s Cloud Technology Stack for Data Science
  • Deep knowledge of Global Product Safety’s SFI Process
  • Extensive NLP solutions from business problem statement to deployment and ongoing optimization
  • Expertise with Large Language Models solutions from business problem statement to cloud deployment that have provided significant incremental business value
  • Experience with generative AI solutions that you have developed and deployed into a production environment that have provided significant incremental business value

Responsibilities

  • Work with our existing business partners to identify opportunities for new AI/ML solutions to enhance processes and enable our team members to be more efficient
  • Train new machine learning models to solve complex business problems.
  • Prototype new AI solutions, including Generative AI, to solve business problems.
  • Develop new Agentic AI solutions to streamline business process
  • Ehnance existing model solutions as business and technology continues to evolve
  • Provide guidance on business problems using statistical methods and can craft ad-hoc reports to share findings and recommendations with business partners.
  • Build statistical models that depict company-wide trends.
  • Perform testing and validation of data sets.
  • Work on and refine storytelling skills to convey ideas and results to leadership, customers and stakeholders
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