Sr. Data Scientist – Machine Learning & AI; Quality, Vehicle & Engineering Analytics

About The Role

We are looking for a Senior Staff Data Scientist (ML/AI) to serve as a technical leader, architect, and individual contributor within the Machine Learning & AI Engineering team s role sits at the intersection of machine learning, advanced analytics, experimentation, and large‑scale vehicle/IoT data systems. You will define and influence how ML and AI are used across vehicle quality, engineering systems, and customer experience outcomes.

It is a high‑impact, senior IC role (Staff/Principal level influence) responsible for shaping technical strategy, designing scalable ML systems, and driving measurable business outcomes such as quality improvement, warranty reduction, and customer experience enhancement.

• Define and evolve the ML/AI architecture and framework supporting quality, engineering, and vehicle analytics across the organization
• Set technical direction for:
  • Machine learning systems
  • Experimentation platforms
  • Data science architecture
• Act as a trusted technical advisor to senior leadership on:
  • Model feasibility
  • Trade-offs (accuracy, scalability, cost, interpretability)
  • Business impact of ML/AI initiatives
• Influence roadmap decisions across engineering and product organizations

• Develop and deploy predictive, prescriptive, and causal models using:
  • Vehicle data
  • IoT sensor data
  • Enterprise datasets
• Apply advanced techniques including:
  • Statistical modeling
  • Machine learning algorithms
  • Deep learning / neural networks
• Lead root cause analysis for vehicle quality, performance, and system failures
• Design and build LLM-based systems and agentic AI solutions for engineering and quality use cases

• Architect and guide development of large-scale distributed data and ML systems
• Build and scale analytics pipelines using Spark-based distributed processing frameworks
• Lead ML model lifecycle management, including:
  • Training
  • Validation
  • Deployment
  • Monitoring in production
• Ensure models and systems are:
  • Explainable
  • Reliable
  • Production-ready
  • Compliant with automotive/regulatory standards

• Own and evolve the experimentation framework/platform for safe, scalable testing of vehicle and software features
• Design statistically sound experiments (A/B tests and beyond)
• Translate experimental results into clear product and engineering decisions
• Drive measurable business outcomes including:
  • Warranty cost reduction
  • Improved product quality
  • Enhanced customer experience
  • Revenue‑impacting insights

• Mentor senior and mid-level data scientists, raising technical standards across the team
• Help teams with:
  • Problem formulation
  • Research design
  • Statistical interpretation
• Contribute to internal knowledge systems and external-facing technical content (e.g., blogs or papers)
• Serve as a cross‑functional leader bridging engineering, product, and executive teams

• Proven impact from deployed ML systems or production analytics products
• Quantifiable improvements in:
  • Vehicle quality
  • Warranty reduction
  • Customer experience metrics
• Ability to influence technical strategy beyond their immediate team
• Strong communication skills with executive and non‑technical stakeholders
• Demonstrated ability to turn complex analysis into business decisions and outcomes

• Bachelor’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field
• A minimum of 8 years of experience in data science, advanced analytics, or machine learning, including at least 5 years hands‑on with Databricks, Palantir, Snowflake, or AWS Sage Maker
• Expert‑level proficiency in:
  • Python (or R)
  • SQL
• Strong foundation in:
  • Machine learning algorithms
  • Statistical modeling
  • Neural networks / deep learning
• Experience building ML solutions on distributed systems (e.g., Spark)

• Master’s degree in Computer Science, Computer Engineering, Electrical Engineering, or a related field
• Experience with:
  • Large Language Models (LLMs)
  • Fine‑tuning foundation models
  • Agentic AI systems
• Experience building ML solutions in…