Senior Manager Data Science

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.

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.