Data Scientist

Ford GDI&A Industrial Systems Analytics is seeking hands-on data scientists who can develop and deliver practical, reliable data products. You will apply statistical analysis, machine learning, and modern AI—including agentic applications—to automotive and industrial use cases, from exploration and prototyping through deployment and ongoing improvement.

You'll be working as a technical expert in a variety of products involving tabular data predictions, agentic classification/assistance, semantic/search and summarization, platform cloud deployment, and CI/CD pipelines among others.

Responsibilities

Responsibilities:

• Design ML data products by following robust coding practices

• Analyze, clean, and prepare structured and unstructured data for modeling and AI applications.

• Perform Statistical Analysis and preprocessing on large datasets

• Explore SoTA models/techniques so that they can be applied for automotive industry use cases

• Conduct ML experiments to train/infer models; if need be, build models that abide by memory & latency restrictions

• Deploy REST APIs or a minimalistic UI applications using Docker and Kubernetes tools

• Showcase Machine Learning, Deep Learning and GenAI applications in the best way possible to users through web frameworks (Dash, Plotly, Streamlit, etc.,)

• Build modular AI/ML products that could be consumed at scale.

• Develop agentic applications using Google’s Agent Development Kit (ADK), integrating agents with tools, data, and application workflows.

• Use Git and GitHub for version control, collaboration, code review, and project workflows.

• Communicate findings, trade-offs, and recommendations to technical and non-technical partners.

Qualifications

Experience: 3+ years

Education: Bachelor’s or Master’s Degree in Computer Science, Engineering, Maths or Science 

Core Technical Requirements:

Python and Data Science:

• Strong Python skills; experience with NumPy, Pandas, and scikit-learn. Ability to write readable, testable, and maintainable code.

Machine Learning and Statistics:

• Experience selecting, training, and evaluating appropriate models; understanding of data preparation, validation, metrics, and common sources of bias or leakage.

Application Development:

• Experience developing APIs or services with FastAPI or comparable frameworks. Ability to build clear, useful interfaces with Streamlit or similar tools.

Google Cloud:

• Experience deploying or supporting data and AI workloads on GCP. Relevant services include:

• Vertex AI: model development, evaluation, and managed AI capabilities.

• Cloud Run: deploying containerized APIs and applications.

• Cloud Storage: data and model artifacts.

• BigQuery: analytical data storage and SQL-based analysis.

• Artifact Registry and Cloud Build: container images and build workflows.

• Cloud Logging and Cloud Monitoring: observability.

• Secret Manager and IAM: secrets and access control.

Specific services depend on the solution; candidates are not expected to have used every service.

Agentic AI:

• Familiarity with Google’s Agent Development Kit (ADK), or comparable agent frameworks and a willingness to learn ADK.

Git and GitHub:

• Comfortable with branching, commits, pull requests, code review, and collaborative development.

Helpful Additional Experience:

• Deep learning, natural-language processing, generative AI, or retrieval-based applications.

• Docker and container-based development.

• Automated testing, dependency and environment management, CI/CD, or production monitoring.

• Data pipelines, distributed processing, or cloud-based analytics.

• Vector search, search platforms, or unstructured data.

Collaboration:

We value clear communication, thoughtful technical judgment, teamwork, and the ability to explain complex results to a broad audience. Candidates should be comfortable collaborating across teams and locations using GitHub, chat, email, and video calls.