Data Scientists Modeling Location: Dearborn, MI/ Hybrid Duration: 36 months with possible extension. Description: Go-to-marketing-modeling (GTMM) team within NASA of Client Global Data, Insight and Analytics (GDI&A) organization is looking for a highly skilled Machine Learning Engineer to assist in all levels of problem formulation, model development, evaluation, and deployment. NASA(North America Sales Analytics) is a cross-functional team within Client GDI&A that focuses on market demand sensing/analysis, new/used car price/sales analysis and forecast (short to mid-term), business plan (mid to long term) analysis etc.. GTMM team applies advanced machine learning models and systematic econometrics models to solve a wide variety of challenging problems in automotive sales/incentive/price/inventory/production planning, evaluation, and optimization to help business make relevant and optimal decisions. Key Responsibilities: Design, develop, and deploy machine learning models and algorithms. Collaborate with data engineers to design and maintain scalable data pipelines. Conduct data preprocessing, including collection, cleaning, and feature engineering. Implement machine learning models and monitor their performance. Stay updated with the latest trends and advancements in AI and machine learning. Collaborate with cross-functional teams to understand business requirements and provide AI/Client solutions. Develop tools and processes to monitor and analyze model performance and data accuracy. Required Qualifications: Master’s degree in computer science, Engineering, Mathematics, Statistics or a related field. 1+ years of post-graduate experience in machine learning, data science, or a related field. Strong programming skills in Python and familiarity with Client libraries (e.g., TensorFlow, PyTorch). Experience with data modeling, data architecture, and ETL processes. Strong understanding of machine learning algorithms and principles. Excellent problem-solving and analytical skills. Ability to work in a fast-paced, team-oriented environment. Skills Preferred: Experience with cloud platforms (e.g., AWS, Azure, GCP). Knowledge of containerization technologies (e.g., Docker, Kubernetes). Familiarity with DevOps and MLOps practices. Experience with demand forecast and pricing strategy Education Required: Master’s degree in computer science, Engineering, Mathematics, Statistics or a related field. Education Preferred: Ph.D. in a quantitative field such as Computer Science, Engineering, Mathematics, Statistics or a related field Call Notes: Manager Notes: (Must Haves) Hands-on/Working experience with the following requirements below: Cloud platform experience GCP is preferred GCP is what they currently use. Familiarity with DevOps and MLOps practices Experience with demand forecast and pricing strategy Strong programming skills in Python (preferred) will accept C++ or Java experience. SQL- required Machine learning Education: Master’s degree in computer science, Engineering, Mathematics, Statistics or a related field with 3 year working exp. Project details: This project is a group collaboration at Client. The HM wants a candidate that can work as a group but also lead.