Senior Specialist, Data Scientist

AIA Hong Kong and Macau; Singapore; On-site; SGD 90,000 - 120,000; Full time

Job summary

AIA Hong Kong and Macau is seeking a Senior Data Scientist (Specialist) in Singapore to lead the design, development, and deployment of advanced analytics and machine learning solutions. The role requires 8–10 years of experience in data science and a deep understanding of Python, SQL, and various cloud platforms.

Candidates should have a Bachelor's or Master's degree in a related field, with a strong preference for those holding a PhD. Join a dynamic team and make impactful contributions across healthcare and insurance domains.

Qualifications

• 8–10 years of industry experience in data science.
• Strong programming skills in Python and SQL.
• Experience with machine learning and statistical modeling.

Responsibilities

• Design and deploy advanced analytics and ML solutions.
• Collaborate with stakeholders to define ML problem statements.
• Ensure compliance with data privacy and security regulations.

Skills

Python/PySpark

SQL

Machine Learning & Statistics

Deep Learning

Cloud & Data Platforms

Education

Bachelor's/Master's degree in data science or related field

PhD in a quantitative field

Tools

Microsoft Azure

Azure Databricks

Docker

Kubernetes

Job description

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. The role requires a blend of hands‑on technical expertise, curiosity, problem solving, and business acumen, and involves end‑to‑end delivery for AIML workstreams from scoping to deployment and monitoring.

Responsibilities

• AI & ML 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 required data, quality thresholds, labeling strategy, north‑star metrics, decision boundaries, counterfactuals, and baselines.
  • Connect model metrics to business outcomes and maintain an ML System Design Spec.
• AI & ML Model Development, Research & Deployment
  • Explore data using Python, PySpark, SQL, and visualization libraries.
  • Engineer high‑quality features with domain knowledge, statistical transformations, and automated selection.
  • Design, implement, and validate models for complex healthcare and insurance challenges, applying cutting‑edge algorithms.
  • Collaborate with DevOps for productionization (Docker, Kubernetes, CI/CD pipelines) and implement monitoring for drift, performance, and data quality.
  • Build and maintain reusable ML accelerators (Cookiecutter, Feature Engineering Toolkit, AutoML, Unified Evaluation Harness, Observability Blueprints, Responsible AI Pack).
• Collaboration & Stakeholder Engagement
  • Work with actuaries, clinicians, engineers, and product managers to align solutions with objectives.
  • Facilitate technical workshops and presentations for diverse audiences.
  • Serve as subject matter expert on analytics and data science methodologies.
• Governance & Compliance
  • Ensure data privacy regulations and security best practices are followed.
  • Implement responsible AI, fairness, explainability, bias detection, audit trails, and documentation for regulatory compliance.

Candidate Profile

• Bachelor’s/ master’s degree in data science, statistics, applied mathematics, computer science, or a related field and 8–10 years of industry experience.
• Highly Preferred: PhD in a quantitative field; advanced certifications in Microsoft Azure and modern data/ML platforms.

Technical Expertise

• Programming & Data Foundations: Python/PySpark, SQL, Excel, reproducible analytic workflows.
• Analytical Rigor & Problem Solving: evaluation taxonomies, acceptance criteria, timing, code‑based playbooks.
• Machine Learning & Statistics: supervised/unsupervised modeling, time‑series, deep learning, NLP, ensemble methods, GenAI application, experimental design.
• Model Deployment & MLOps: productionization, reusability, scalability, cloud-based pipeline interfaces.
• Cloud & Data Platforms: Microsoft Azure, Azure Databricks, scalable data processing.
• Governance, Privacy & Responsible AI: data privacy/security best practices, responsible AI principles, documentation and audit trails.
• GenAI‑first & Vibe Coding: Vibe‑coding workflow, agentic AI tools, design specs, model cards, experiment summaries, runbooks.

Competencies & Core Characteristics

• Technical Domain Expertise: mastery of data science methodologies, programming languages, and cloud‑based AIML platforms.
• Analytical Rigor & Problem Solving: translating datasets into actionable insights with high standards of accuracy.
• Unifier & Cross‑Functional Influencer: drives roadmaps, aligns multiple teams toward shared model goals.
• Adaptable & Resilient Operator: operates in high ambiguity, prioritizes pragmatically, manages risk to land outcomes at speed.
• Curiosity & Innovation: 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 documentation practices.