Note: The job is a remote job and is open to candidates in USA. Lorven Technologies is advertising a Lead Data Scientist role focused on applied AI and data science for healthcare and underwriting. The role leads end-to-end model development and production, contributes to internal AI products and shared model-governance practices, and partners with business, AI, and engineering teams to deliver measurable outcomes.
Responsibilities
- The Lead Data Scientist will independently lead applied AI and data science workstreams that translate complex healthcare and underwriting data into measurable improvements in risk selection, pricing accuracy, operational efficiency, and the underwriting experience
- The role will personally lead work from problem framing, target definition, feature engineering, and model development through rigorous validation, production deployment, monitoring, and business-impact measurement
- The Lead Data Scientist will operate with broad autonomy, manage workstream plans and risks, and bring major methodological, governance, or cross-platform decisions to the VP of AI and VP Analytics when a decision has broader business impact
- In addition to delivering models, the Lead Data Scientist will help build internal AI products and contribute improvements to shared practices for temporal validation, explainability, responsible AI and data use, model governance, and performance monitoring. Working with the VP of AI and VP of Analytics, the role will align assigned workstreams to the broader scientific roadmap and escalate decisions with cross-workstream impact. The role will partner with Business, AI, and Engineering to translate complex evidence into clear recommendations and measurable business outcomes
Skills
- Bachelor's or master's degree in Statistics, Data Science, Computer Science, Mathematics, Engineering, or a related quantitative field
- 8+ years in data science, machine learning, statistics, actuarial analytics, including substantial work with healthcare, pharmacy, insurance risk, or sensitive longitudinal data and a proven record of independently owning models or analytical workstreams end to end
- Advanced Python and SQL, with experience in scikit-learn, XGBoost, GBMs, or comparable frameworks
- Deep expertise in Supervised and Unsupervised Machine Learning methods, explainability, statistical distributions, rare-event and high-cost modeling, calibration, and optimization
- Proven ownership of production models and the MLOps lifecycle on AWS or a comparable cloud platform, including Git-based version control, testing, deployment, monitoring, retraining, rollback, documentation, and responsible AI/model-governance practices
- Strong business acumen and judgment, with the ability to connect analytical outputs to measurable business outcomes, influence cross-functional decisions, and navigate trade-offs among model quality, risk, cost, scalability, and time to value
- An advanced degree is a plus
- Experience using PySpark or comparable distributed-computing tools to work with structured and unstructured data at scale
- PyTorch experience is a plus
- Familiarity with Kedro or a similar pipeline framework is a plus
- Experience with LLMs, prompt engineering, RAG, embeddings, vector DB, or agentic frameworks is helpful but not required
Benefits
- Remote work
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