Data Scientist

• Design ML data products using 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 state-of-the-art models and techniques for automotive industry use cases
• Conduct machine learning experiments to train and infer models, including models subject to memory and latency restrictions
• Deploy REST APIs or minimalistic UI applications using Docker and Kubernetes
• Showcase machine learning, deep learning, and generative AI applications through Dash, Plotly, Streamlit, and similar web frameworks
• Build modular AI/ML products for scalable consumption
• 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

• 3+ years of experience
• Bachelor’s or Master’s Degree in Computer Science, Engineering, Maths or Science
• Strong Python skills; experience with NumPy, Pandas, and scikit-learn
• Ability to write readable, testable, and maintainable code
• Experience selecting, training, and evaluating machine learning models
• Understanding of data preparation, validation, metrics, and common sources of bias or leakage
• Experience developing APIs or services with FastAPI or comparable frameworks
• Ability to build interfaces with Streamlit or similar tools
• Experience deploying or supporting data and AI workloads on Google Cloud Platform
• Familiarity with Google’s Agent Development Kit (ADK), or comparable agent frameworks and willingness to learn ADK
• Comfortable with Git and GitHub branching, commits, pull requests, code review, and collaborative development
• Helpful experience with deep learning, natural-language processing, generative AI, retrieval-based applications, Docker, automated testing, dependency and environment management, CI/CD, production monitoring, data pipelines, distributed processing, cloud-based analytics, vector search, search platforms, or unstructured data
• Comfortable collaborating across teams and locations using GitHub, chat, email, and video calls