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Data Scientist at Ford Global Career Site
Dearborn, Michigan, United States -
Full Time

Start Date

Immediate

Expiry Date

07 Jan, 26

Salary

0.0

Posted On

09 Oct, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Data Science, Machine Learning, SQL, Docker, REST APIs, Flask, FastAPI, Terraform, Forecasting Algorithms, Anomaly Detection, Optimization Models, Generative AI, Natural Language Processing, Deep Learning, TensorFlow, PyTorch

Industry

Motor Vehicle Manufacturing

Description
Design, develop, and implement end-to-end AI/ML pipelines, from data ingestion and preprocessing to model training, evaluation, and deployment. Collaborate internally and externally to identify new and novel data sources and explore their potential use in developing actionable business results Established and active employee resource groups Expertise in SQL for data querying, manipulation, and database interaction. Experience with containerization (e.g., Docker), constructing REST APIs using frameworks like Flask or FastAPI,, and adherence to best software development practices (e.g., version control, testing). Ph.D. in Data Science, Computer Science, Business Analytics, Machine Learning, Statistics, or a related quantitative field. Experience working with Terraform to provision Infrastructure as Code Experience in electrification and/or transportation domain. Experience in forecasting algorithms, anomaly detection algorithms, optimization models Experience in Generative AI, including a strong understanding of ML frameworks, algorithms, and practical implementation. Experience in specialized areas such as Natural Language Processing (NLP), deep learning (e.g., TensorFlow, PyTorch), or recommendation systems. A record of publications or presentations in recognized journals or conferences. Inquisitive, proactive, and interested in learning new tools and techniques Comfortable working in a fast-paced and innovative environment where problems are not always well-defined
Responsibilities
Design, develop, and implement end-to-end AI/ML pipelines, from data ingestion and preprocessing to model training, evaluation, and deployment. Collaborate internally and externally to identify new and novel data sources and explore their potential use in developing actionable business results.