Lead Data Scientist

We’re hiring a Lead Data Scientist to join an End-to-End Optimization team focused on streamlining complex operational workflows that require a mix of automation and human review. In this role, you’ll design and build production-grade data science solutions that improve speed, accuracy, and efficiency across decision pipelines—partnering closely with product, engineering, and operations stakeholders to turn ambiguous problems into scalable systems. What you’ll do • Own end-to-end data science solutioning: problem framing → data exploration → modeling → deployment → monitoring and iteration • Build and enhance predictive analytics / ML models in Python to optimize workflow outcomes and throughput • Create monitoring and performance frameworks (data quality, drift, model performance, KPI health) and translate findings into actionable recommendations • Break down complex business problems into measurable hypotheses, experiments, and deployable solutions • Provide technical guidance and mentorship to 2–3 junior data scientists and data engineers • Collaborate with engineers on productionization (APIs, batch pipelines, orchestration, CI/CD) What we’re looking for • Advanced proficiency in Python and strong foundations in predictive modeling / machine learning / statistical modeling • Proven experience delivering end-to-end, production data science solutions (not just notebooks) • Cloud experience in GCP, AWS, and/or Azure (GCP preferred) • Strong software engineering and coding practices (version control, testing, scalable design) • Experience in operations/logistics/process optimization (supply chain, call centers, workflow efficiency, etc.) is highly valued • Excellent communication skills; ability to set vision and execute hands-on Nice to have • Healthcare experience (optional) • Familiarity with MLOps patterns (model registries, automated retraining, observability, feature stores)