Senior Data Scientist

My client is a Digital Health‑focused company based in Singapore. This role is central to designing, developing, and deploying advanced analytics and machine learning solutions that deliver meaningful insights across healthcare, insurance, and wellness. The Senior Data Scientist works closely with engineers, actuaries, clinicians, and product managers to turn complex datasets into predictive models and decision‑support tools that improve outcomes and efficiency.

It requires a mix of technical depth, curiosity, problem‑solving, and business acumen. Responsibilities span end‑to‑end AIML delivery from scoping to deployment and monitoring, leading feature engineering strategy, mentoring juniors, and conducting code reviews while remaining hands‑on. The right candidate thrives in agile environments and is motivated by solving real‑world healthcare challenges with data.

Responsibilities

1. AIML System Design and Product Thinking • Translate business questions into ML problem statements (classification, ranking, forecasting, optimization, GenAI workflows). • Define required data sources, quality thresholds, and labelling strategies. • Establish metrics, baselines, and decision boundaries to connect model performance with business outcomes. • Maintain ML system design specifications including hypotheses, constraints, acceptable risk, and guardrails.
2. Model Development, Research, and Deployment • Explore and engineer features using Python, pyspark, SQL, and visualization tools. • Build and validate models across healthcare and insurance use cases, applying advanced algorithms where relevant. • Ensure reproducibility and scalability through modular design and documentation. • Collaborate with DevOps to productionize models using Docker, Kubernetes, and CI/CD pipelines. • Implement monitoring for drift, performance, and data quality; define retraining strategies. • Develop reusable ML accelerators to standardize workflows.
3. Collaboration and Stakeholder Engagement • Partner with cross‑functional teams to align technical solutions with strategic goals. • Lead workshops and presentations to ensure clarity and buy‑in. • Act as a subject matter expert on analytics and data science practices.
4. Governance and Compliance • Ensure adherence to data privacy and security standards. • Incorporate fairness, explainability, and bias detection into model development. • Maintain documentation and audit trails for compliance.

Education and Experience

• Bachelor's or Master's in Data Science, Statistics, Applied Mathematics, Computer Science, or related field with 8–10 years of industry experience.

• PhD and advanced certifications in modern data/ML platforms are highly preferred.

• This role is based in Singapore and requires candidates who already hold valid local work authorization.

Technical Expertise

• Strong skills in Python, pyspark, SQL, and scalable data handling.

• Experience in defining evaluation frameworks and balancing trade offs in accuracy, fairness, latency, and interpretability.

• Hands on knowledge of supervised/unsupervised learning, time series, deep learning, NLP, ensemble methods, and practical GenAI applications.

• Proven track record in end to end ML lifecycle delivery and production deployment.

• Experience with cloud platforms, especially Azure Databricks.

• Familiarity with Responsible AI principles, documentation standards, and compliance practices.