Applied Data Scientist

Responsibilities

• Define and manage data collection requirements for the ADAS organization, addressing both field issues and the development of innovative new features.
• Collaborate with Ford’s Connected Vehicle Data Enablement (CVDE) team to implement custom data collection strategies.
• Analyze large-scale datasets in GCP using Big Query (SQL) and Python.
• Develop data products and Machine Learning models by fusing multi-domain sources, including CVDE data, warranty claims, customer verbatims, weather, and road/lane geometry.
• Execute ML model inference and perform sensitivity analysis on data products to develop a deep understanding of the data.
• Democratize insights across the organization through automated “Push Analytics.”
• Build Text-to-SQL and code-generation/execution AI tools to enable “Custom Pull Analytics,” allowing Subject Matter Experts (SMEs) to retrieve bespoke insights.
• Develop interactive AI/ML applications using the Python ecosystem (e.g., Chainlit, Dash, or Streamlit) and design dashboards in Superset, PowerBI, or Looker Studio for standardized reporting.

• Education:
  
  Bachelor’s degree in data science, Computer Science, Statistics, Mathematics, or a related Engineering field.
• Experience:
  
  Minimum 3+ years of professional experience in Data Science, Machine Learning, or Data Engineering.
• Programming & Data:
  Proficiency in Python and advanced SQL for data manipulation and analysis.
• Cloud
  
  Experience:
  
  2+ years of hands‑on experience working with large-scale datasets in a cloud environment (preferably GCP/Big Query).
• Machine Learning:
  Proven experience building, training, and running inference on Machine Learning models to solve real-world problems.
• Data Engineering:
  Ability to perform "Data Fusion" by joining and cleaning disparate, multi-domain datasets.
• Visualization:
  Experience creating data visualizations or dashboards using tools such as PowerBI, Looker Studio, or Superset.
• Significant Exposure to AI tooling and eco system: 1+ years of experience with Generative AI technologies, specifically building RAG pipelines and Text-to-SQL or Code-Generation applications.
• Communication:
  Strong ability to translate business requirements from Subject Matter Experts (SMEs) into technical data collection and analysis plans.