Senior Specialist, Data Scientist
Do meaningful work with us. Every day.At Amplify Health, we’re looking for individuals with ambition, resilience and passion for healthcare, insurance, wellness and digital technology. As a fast-growing business with the ambition of making people and communities across Asia healthier, we have exciting career opportunities available to help us achieve our vision.The Senior Data Scientist (Specialist) plays a pivotal role in designing, developing, and deploying advanced analytics and machine learning solutions that deliver actionable insights across healthcare, insurance, and wellness domains. This individual collaborates with cross-functional teams—including data engineers, actuaries, clinicians, and product managers—to transform complex datasets into predictive models and decision-support tools that improve health outcomes and operational efficiency.The role requires a blend of hands-on technical expertise, curiosity, problem solving and business acumen. The Senior Data Scientist is responsible for end-to-end delivery for AIML workstreams from scoping to deployment/monitoring; leads feature engineering strategy; mentors juniors and performs code reviews on top of being hands on. The ideal candidate thrives in a fast-paced, agile environment and is passionate about leveraging data to solve real-world healthcare challenges.Responsibilities1) AIML System Design, Business Problem Framing & Product ThinkingPartner with stakeholders to clarify business questions into ML problem statements (classification, ranking, uplift, forecasting, optimization, GenAI RAG/agentic workflows, etc.).Define what data is needed (first-/third-party, events, text, image, claims/transactions, IoT), data quality thresholds, and labelling strategy.Define north-star metrics (online and offline) and decision boundaries; craft counterfactuals and baselines (e.g., business-as-usual) to quantify impact. Connect model metrics to business outcomes.Write and maintain an ML System Design Spec: problem hypothesis, decision loop, users, constraints, acceptable risk, SLAs/SLOs, and post-deployment guardrails.2) AI and ML Model Development, Research & DeploymentData Exploration & Feature Engineering:- Conduct advanced exploratory data analysis on large datasets using Python, pyspark, SQL, and visualization libraries.- Engineer high-quality features leveraging domain knowledge, statistical transformations, and automated feature selection techniques.Model Development:- Design, implement, and validate machine learning and statistical models to address complex healthcare and insurance challenges.- Explore cutting-edge algorithms (e.g., regression, clarification etc.) and assess their applicability to real-world use cases.- Ensure reproducibility and scalability of models through modular design and robust documentation.Model Deployment & MLOps- Collaborate with DevOps engineers to productionize models using containerization (Docker), orchestration (Kubernetes), and CI/...