Principal Data Scientist

• Translate ambiguous business problems into well-scoped data science solutions with clear success metrics.
• Design end-to-end solutions, from data requirements and modeling approach to deployment and monitoring.
• Build reference implementations, reusable components and templates the team can extend.
• Make build, buy and reuse decisions, and balance accuracy against cost, speed and maintainability.

Enabling the team to implement

• Write clear design documents, runbooks and code standards so solutions can be built and supported by others.
• Review code, models and experiments; set the bar for quality and reproducibility.
• Mentor data scientists and ML engineers, and run knowledge-sharing sessions.
• Hand over solutions cleanly, then stay available to unblock the team through delivery.

Onshore/offshore collaboration

• Work with onshore and offshore teams as one unit, with shared standards, tooling and ways of working.
• Run design reviews and planning sessions that suit multiple time zones, with clear written decisions.
• Keep context flowing between business stakeholders and offshore delivery teams.

Advanced analytics, ML and GenAI

• Apply statistical modeling, machine learning and forecasting to problems such as prediction, optimization and segmentation.
• Identify and prototype GenAI use cases (for example summarization, document extraction, natural-language data access) and take proven ones to production.
• Define evaluation methods for models and LLM output, including accuracy, bias, drift and cost.

Governance and stakeholder partnership

• Advise senior leaders on what data science can and cannot deliver, in plain language.
• Work with data engineering and platform teams on pipelines, MLOps and model monitoring.
• Uphold responsible-AI, privacy and security standards across all solutions.

• Bachelor’s or Master’s in Data Science, Statistics, Computer Science, Mathematics or a related quantitative field (or a bachelor’s with equivalent experience).
• 9+ years in data science or applied machine learning, with a record of solutions running in production.
• Strong Python and SQL; experience with common libraries such as scikit-learn, pandas, PyTorch or TensorFlow.
• Deep knowledge of statistics, experimental design and core ML techniques (supervised, unsupervised, time series, NLP).
• Proven experience designing solutions that other teams implemented and maintained, with documented patterns and handoff.
• Experience leading or mentoring data scientists and engineers, formally or as a technical lead.
• Experience working with distributed teams across time zones.
• Clear communication with both technical teams and business executives., * Hands-on experience with LLMs: prompt design, retrieval-augmented generation (RAG), fine-tuning and evaluating output quality.
• MLOps experience: model versioning, CI/CD for ML, monitoring and retraining (for example MLflow, Kubeflow, SageMaker, Azure ML, Vertex AI).
• Experience with cloud data platforms such as Snowflake, Databricks, BigQuery or Microsoft Fabric.
• Familiarity with Spark or other distributed processing frameworks.
• Domain experience in healthcare industry.
• Publications, patents or open-source contributions.
• Relevant cloud or ML certifications.