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 Data Scientist plays a pivotal role in how Amplify Health measures and communicates value from our claims data analytics products for provider management. Sitting within the Data & Analytics team, this person owns the design of core metrics and the broader analytics framework, shapes the analytics narrative behind our product (built out in-product by our application engineering team), and partners with engineering to keep our semantic layer trustworthy.

You will also help define and evaluate the quality of our analytics agents, working closely with product, engineering, and clinical partners, and will write production-quality code yourself when a problem calls for it.

The ideal candidate combines strong analytical craft and business judgement with the technical depth to partner credibly with engineering.

Responsibilities

1) Metrics & Analytics Framework

• Own the design of core business metrics and the analytics framework used across our claims data analytics product for provider management.
• Translate business logic into clear, well-governed metric definitions.
• Partner with engineering to review and maintain the semantic layer against the metrics and analytics framework.

2) Analytics Storytelling

• Design the analytics narrative behind each dashboard — what it should show and why — for our application engineering team to build out in-product.
• Work with product and clinical teams to ensure that narrative reflects real business questions.
  

3) Agent Evaluation

• Partner with the AI Engineer to define evaluation business cases for our analytics agents.
• Design and run evaluations of agent outputs against business and quality criteria, feeding results back into the roadmap.

4) Production Analytics

• Write production-quality analytics code when the business case calls for it, working closely with the Machine Learning Engineer.
• Contribute to code review and quality practices across the team's analytics codebase.

Candidate Profile

Experience and Qualifications

• ~6–9 years of experience in data science or analytics roles, ideally within data-rich or regulated industries.
• Track record of designing metrics frameworks and analytics narratives that others can build into product dashboards.
• Experience partnering with engineering teams on semantic layers or metric definitions in a modern data stack.
• Production-level Python and PySpark; comfortable using SQL for temporary or ad hoc analysis (SQL is not used at the production level).
• Working knowledge of machine learning and data pipeline concepts (feature engineering, training, inference) — enough to understand how gold-layer data is produced and collaborate effectively with the Machine Learning Engineer.
• Bachelor’s degree in a quantitative field (Data Science, Statistics, Computer Science, or related); advanced degree a plus.

Nice to Have

• Prior experience with healthcare, insurance, or claims data.
• Experience stepping into a team-lead capacity, coordinating workstreams across a small analytics team.

Competencies & Core Characteristics:

We are seeking professionals who embody the following competencies and characteristics essential for success in our scale-up environment:

• Technical Domain Expertise: Comfortable across metrics design and semantic layers, with enough engineering fluency in Python/PySpark to write production code when needed.
• Analytical Storyteller: Turns complex data into clear, decision-ready narratives for varied audiences.
• Unifier & Cross-Functional Influencer: Partners naturally with product, engineering, and clinical teams to align on what “good” looks like.
• Curiosity & Rigor: Questions assumptions, validates metrics against business reality, and iterates.

You must provide all requested information, including Personal Data, to be considered for this career opportunity. Failure to provide such information may influence the processing and outcome of your application. You are responsible for ensuring that the information you submit is accurate and up-to-date.