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.
Responsibilities
1) AIML System Design, Business Problem Framing & Product Thinking
- Partner 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 & Deployment
- Data 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/CD pipelines.
- Collaborate with DE team to implement and optimize the feature store
- Implement automated monitoring systems for model drift, performance degradation, and data quality issues.
- Define retraining strategies and governance protocols to ensure compliance and long-term reliability. - AI/ ML Accelerators development:
- Build and maintain reusable ML accelerators (Cookiecutter, Feature Engineering Toolkit, AutoML , Unified Evaluation Harness, Observability Blueprints, Responsible AI Pack etc) that standardize feature engineering, model training, and evaluation across tasks.
3) Collaboration & Stakeholder Engagement
- Partner with cross-functional teams—including actuaries, clinicians, engineers, and product managers—to align technical solutions with strategic objectives.
- Facilitate technical workshops and presentations to ensure clarity and buy-in across diverse audiences.
- Act as a subject matter expert on analytics, data science methodologies and best practices.
4) Governance & Compliance
- Ensure adherence to data privacy regulations and implement security best practices across all data science workflows.
- Advocate for responsible AI by incorporating fairness, explainability, and bias detection into model development.
- Maintain comprehensive audit trails and documentation for regulatory compliance and internal governance.
Candidate Profile
Experience and Qualifications
- Required: Bachelor’s/ master’s degree in data science, Statistics, Applied Mathematics, Computer Science, or a related field and around 8 to 10 years of industry experience
- Highly Preferred: PhD in a relevant quantitative field. Advanced certifications in Microsoft Azure and modern data/ML platform highly preferred.
Technical Expertise
Programming & Data Foundations
- Strong proficiency in Python/ Pyspark (data wrangling, EDA, modeling) and SQL for working with large, complex datasets; advanced Excel for analysis and validation.
- Reproducible analytic workflows (modular code, notebooks, documentation) and robust data handling across heterogeneous sources.
Analytical Rigor & Problem Solving
- Experience in defining evaluation taxonomies and acceptance criteria across initiatives; balances statistical and operational risk.
- Experience in codifing analytical playbooks and institutionalizes measurement frameworks across products/teams. Arbitrates trade-offs (accuracy, fairness, latency, interpretability) for high impact decisions.
Machine Learning & Statistics
- Experience in anticipating confounding/selection bias; uses robust offline protocols (nested CV, back testing, temporal splits), designing sensitivity analyses, ablation studies, and error analysis to guide iteration.
- Advanced hands-on knowledge of application of Supervised/unsupervised modelling, time series modelling, deep learning, NLP and ensemble methods for magnitude of use cases. Practical application knowledge of GenAI where applicable.
- Experience in leading the design and scoping of end‑to‑end high stake ML lifecycle pipelines. .
- Experience in running multiple projects and conducting/overseeing high stakes experiments and peer reviews for critical models.
- Proven experience in balancing arbitrates trade-offs (accuracy, fairness, latency, interpretability) for high impact decisions.
Model Deployment & MLOps:
- Proven track record of putting model into production and monitoring. Experience in shaping reusability, scalability, and model lifecycle interfaces with platform/engineering.
Cloud & Data Platforms (Microsoft Azure)
- Experience with Azure Databricks, Data bricks, for scalable data processing, model training, and orchestration
Governance, Privacy & Responsible AI
- Knowledge of data privacy/security best practices across workflows.
- Knowledge of applying Responsible AI principles into model building, comprehensive documentation and audit trails for compliance experience.
- Experience in establishes documentation guidelines and review checkpoints
GenAI-first & Vibe Coding
- Experience in GenAI vibe-coding workflow by default (generate–refine–test–document), while maintaining code quality, reviews, and reproducibility.
- Experience in using Agentic AI/ GenAI tools to draft design specs, model cards, experiment summaries, runbooks, and to automate repetitive analysis/engineering tasks to drive measurable efficiency and productivity gains.
Competencies & Core Characteristics:
We are seeking professionals who embodies the following competencies and characteristics essential for success in our scale-up environment:
- Technical Domain Expertise (Modelling): Demonstrates mastery of data science methodologies, programming languages, and cloud-based AIML platforms. Applies advanced techniques to build robust, scalable models and data pipelines that meet business objectives.
- Analytical Rigor & Problem Solving: Demonstrates mastery of translating complex datasets into actionable insights using AI/ML algorithms. Maintains high standards of accuracy and validity in all outputs.
- Unifier & Cross-Functional Influencer: Influences roadmaps; aligns multiple teams toward shared model goals. Communicates effectively to bridge gaps between data science and business needs. Represents DS in cross org forums, shapes data strategy and methodological direction for the portfolio.
- Adaptable & Resilient Operator: Operates in high ambiguity; sequences investments; ensures teams deliver value at pace. Coach’s others in risk management and decision velocity. Delivers under uncertainty and changing scope; prioritizes pragmatically and manages risk to land outcomes at speed.
- Curiosity & Innovation: Proactively explores new methods and tools; runs lean experiments to separate signal from noise and codifies learnings.
- Responsible & Governed AI: Applies privacy-by-design, fairness, transparency, and model documentation practices; champions responsible AI guardrails.
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