PythonSQLPyTorchscikit-learnSparkdbtDatabricksKafkaData EngineeringData ScienceLLMsGenerative AI
What you'll do
Own the decisioning and feedback layer between score and action: action selection and ranking, holdout and experiment assignment, outcome capture, the production ROI calculation, and the pipelines that measure lift per action and channel.
Own the engineering implementation, auditability, and reproducibility of the production savings calculation, in partnership with medical economics and data science, who own the methodology.
Set the cost-to-serve standard for the platform: define per-model and per-decision budgets for compute, streaming, inference, and LLM spend, and make cost a release criterion alongside latency and accuracy.
Define the production bar for ML models and hold releases to it: CI/CD, model registry, alerting, drift, coverage, and calibration by cohort, market, and payer. Engineer the promotion gate a model or ranking must pass before it goes live.
Set technical direction and production standards for our production GenAI applications, including LLM pipelines and agentic workflows, with the evaluation, retrieval, guardrail, and cost controls needed to run them safely on patient data.
Partner with data scientists on modeling and measurement methodology, with medical economics on how savings is defined, and with data and software engineering on shared pipelines, interfaces, and contracts.
