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 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.
#J-18808-Ljbffr