Senior Data Scientist

What you'll be doing:

• 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

What you’ll bring to HeliosX

Machine Learning & Statistical Expertise

• 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)

Technical Stack & MLOps

• 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

Product & Cross-Functional Collaboration

• 3+ years working embedded with product teams translating ML insights into customer-facing features
• 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

Preferred Experience

• PhD or Master's in Statistics, Computer Science, Biostatistics, Health Informatics, or related quantitative field
• Developing clinical prediction models (e.g., readmission risk, adverse events, treatment response, adherence prediction)
• Experience in digital pharmacy, telemedicine, or direct-to-consumer healthcare platforms
• Publication record in healthcare ML or clinical decision support systems
• Experience with GLP-1 medications, weight management, or chronic disease management programs
• Familiarity with LLM applications in healthcare (Claude, GPT-4) for clinical documentation or patient engagement
• Demonstrated knowledge of healthcare regulatory requirements for ML models (FDA guidance on clinical decision support, GDPR, UK MHRA standards)

Why work with us?

At HeliosX, we want to improve healthcare for everyone, and to do this we need a team of brilliant people who share that ambition. We are currently a diverse team of engineers, scientists, clinical researchers, physicians, pharmacists, marketeers, and customer care specialists committed to our mission - but we need more talented folks to join us, if we want to achieve our global ambitions! Aside from working with our all-star team, here are the other benefits of coming on board:

• Generous equity allocations with significant upside potential
• 25 Days Holiday (+ all the usual Bank Holidays)
• Private health insurance, along with extra dental and eye care cover
• Employee Pension with Smart Pensions
• Enhanced parental leave
• Cycle-to-work Scheme
• Electric Car Scheme
• Free Dermatica and MedExpress products every month, as well as family discounts
• Home office allowance
• Access to a Headspace subscription, discounted gym memberships, and a learning and development budget (alongside a free Kindle and audible subscription)

#LI-Hybrid #LI-Senior #LI-DNI