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Data Science & Machine Learning Lead

Mission

Leverage AI and Solera’s data assets to develop, deliver, operate, and maintain innovative, production-grade components that make vehicle claims and ownership simpler, faster, and more more efficient for customers and users.

What you will do

• Lead technical direction for computer vision–based vehicle damage detection (classification, detection, segmentation), plus tree-based models and LLM-powered components.
• Own the ML roadmap: translate business goals into measurable technical plans, milestones, and KPIs.
• Architect scalable data/ML systems on GCP (BigQuery, Dataflow, Vertex AI) to train and serve models across hundreds of millions of images and claims.
• Guide high-quality delivery in a monorepo: reviews, standards, design docs, testing, reproducibility, and CI/CD.
• Drive production MLOps: containerization, GKE/Cloud Run, observability (Grafana), cost/performance tuning, SLOs.
• Shape APIs and services (FastAPI) and internal tools (Streamlit) to accelerate adoption and experimentation.
• Engage cross-functionally with product and platform to prioritize impact and de-risk delivery.
• Balance leadership and hands-on work; scope of people management and IC work is adaptable to your strengths.
• People leadership
• Manage, coach, and grow ML Engineers; run 1:1s, feedback, and career development.
• Foster a culture of clarity, ownership, and high standards; set technical bar via mentorship and example.
• Recruit and onboard top talent; build an inclusive, globally distributed team.

How we work

• Monorepo with strong build system, CI/CD, and code quality practices.
• Freedom to choose the best tool for the job; high autonomy and ownership.
• Production mindset: reliability, observability, maintainability, measurable impact.

Tech stack

• Python; TensorFlow, PyTorch
• GCP: BigQuery, Dataflow, Vertex AI, GKE, Cloud Run, Cloud Deploy
• Docker, Kubernetes
• FastAPI, Streamlit
• Grafana

What you bring

• Proven leadership of ML initiatives from problem framing to production at scale.
• Deep experience with CV models (classification, detection, segmentation) and shipping them with TensorFlow/PyTorch.
• Strong software engineering and MLOps fundamentals: testing, CI/CD, containers, Kubernetes, monitoring.
• Expertise with large-scale datasets and distributed processing on GCP (BigQuery, Dataflow) or similar.
• Experience with tree-based models and integrating LLM APIs into production workflows.
• Track record of setting technical direction, making pragmatic trade-offs, and delivering measurable outcomes.
• Structured problem solving, critical thinking, and a driven, ownership-oriented mindset.
• Effective communication across an internationally distributed team.

Nice to have

• Vertex AI pipelines.
• GPU optimization and cost/performance tuning for training/inference.
• Domain experience in insurance, automotive, or related computer vision applications.