Senior Data Scientist, HeliosX

• 3+ years working embedded with product teams translating ML insights into customer-facing features
• Advanced proficiency in supervised/unsupervised learning, time-series forecasting, survival analysis, and causal inference methods
• Experience building production ML models for patient stratification, risk scoring, or personalized recommendations
• Strong foundation in statistical inference, experimental design, and A/B testing in healthcare contexts
• Expertise in model interpretability techniques (SHAP, LIME) critical for clinical decision support
• Hands-on experience with healthcare-specific ML challenges (imbalanced datasets, missing data, temporal dependencies)
• Proficiency in Python (scikit-learn, pandas, PyTorch/TensorFlow) and SQL for large-scale data analysis
• Experience with modern data platforms (Snowflake, Databricks, or similar cloud data warehouses)
• Demonstrated MLOps capabilities using tools like MLFlow, SageMaker, Vertex AI, or Azure ML
• Experience building real-time ML inference systems and streaming analytics pipelines
• Strong software engineering practices including version control (Git), CI/CD, and model monitoring
• Track record of successful A/B testing and measuring business/clinical impact of ML interventions
• Experience communicating complex technical concepts to non-technical stakeholders (product managers, clinicians, executives)
• Demonstrated ability to balance scientific rigor with pragmatic product delivery timelines

Desirable

• As a Senior Data Scientist at HeliosX, you’ll shape the future of personalised digital healthcare, building and deploying advanced recommendation models that enhance every step of the patient journey
• Working with over a million patient interactions, you’ll turn rich real-world data into actionable insights that improve outcomes and engagement
• Partnering closely with Product, you’ll design and test data-driven solutions using our modern stack (Snowflake, Hex, Claude) to deliver truly individualised care at scale
• Your work will directly influence how millions of patients receive proactive, personalised support as we set the standard for GLP-1–driven digital health globally
• Clinical Prediction Models: Design and implement ML models predicting patient outcomes including medication adherence, treatment response, adverse event risk, and clinical deterioration
• Patient Stratification: Build risk stratification models identifying patients who would benefit from clinical interventions, medication therapy management, or enhanced monitoring
• Treatment Optimization: Develop models recommending optimal treatment pathways, medication alternatives, and personalized clinical interventions
• Healthcare ML Best Practices: Ensure models meet healthcare standards for interpretability, clinical validation, and regulatory requirements (FDA guidance on clinical decision support)
• Product-Embedded Analytics: Lead integration of ML models into customer-facing features improving medication management, adherence tracking, and personalized health recommendations
• Patient Journey Optimization: Build models that personalize patient experiences across online consultation, prescription fulfillment, and ongoing medication management
• Real-Time Clinical Insights: Develop streaming ML capabilities providing real-time patient risk alerts and intervention recommendations
• Cross-Functional Product Leadership: Partner with product managers, clinical teams, and engineers to translate model insights into actionable product features
• Production ML Infrastructure: Establish robust MLOps practices using MLFlow, SageMaker, or similar platforms for model versioning, deployment, and monitoring
• Model Performance Monitoring: Implement comprehensive monitoring for model drift, performance degradation, and clinical safety metrics
• A/B Testing & Validation: Design and execute experiments measuring clinical and business impact of ML-driven interventions
• Regulatory Compliance: Ensure ML models meet healthcare regulatory requirements including model documentation, validation, and audit trails
• Data Science Strategy: Define technical roadmap for healthcare ML capabilities supporting product innovation and clinical outcomes
• Team Development: Mentor data scientists and analysts in healthcare analytics, ML best practices, and clinical domain knowledge
• Research & Innovation: Lead exploration of cutting-edge techniques including causal inference, survival analysis, and federated learning for healthcare applications
• Stakeholder Communication: Translate complex ML concepts and clinical insights into clear recommendations for product, clinical, and executive stakeholders