Role Description As a Data Scientist with strong R programming and R Shiny experience, you will develop data solutions that support clinical trial delivery, data review and decision-making. You will build dashboards, visualisations and analytical tools that turn complex data into clear, actionable insights. - Develop R and R Shiny dashboards and applications for clinical trial data review and decision-making - Build reusable R code, functions and workflows to improve consistency and automation - Create data visualisations and reports that translate complex data into actionable insights - Enhance R Shiny applications through testing, documentation and user feedback - Identify opportunities to improve efficiency through automation and reusable tools Qualifications - Experience working within a clinical trials environment (CRO, pharma or academia) - Previous experience with R Programming & R shiny - Experience in CDISC is highly desirable - MSc or higher in Statistics, Mathematics or related field Benefits - Remote working and flexible working hours - Career development, mentorship, and continuous learning - Supportive, friendly, and collaborative culture - Making a Positive Impact: As a B-Corp™ organization, we are committed to sustainability, solid responsibility and strong ethical governance through our ESG initiatives Company Description Phastar is an award-winning biometrics and data science CRO, trusted by pharma, biotech, and medical device companies worldwide. With a global team of data specialists, we bring expertise, precision, and pace to every trial, because behind every data point is a patient waiting for treatment. We transform complex data into clear, actionable intelligence, helping accelerate drug development, and bring life-changing therapies to patients faster. - Awards & Recognition - SCRIP – Best Contract Research Organization, Specialist Provider - Citeline – 2025 Award Winner - Fierce CRO Awards – Recognized Industry Leader

• Lead Data Analytics & Modeling: Drive data initiatives using both traditional machine learning and emergent AI technologies. Focus on pragmatic, non-generic applications that empower human decision-making and optimize our platform. • Data Pipeline Engineering: Work closely with the core engineering team to design, build, and maintain scalable data pipelines that support ML tooling, analytics, and high-velocity ad delivery systems. • Cross-Functional Collaboration: Leverage your software engineering proficiency to translate data science concepts into production-ready architecture, ensuring seamless integration between data models and backend systems. • Experimentation & Optimization: Design, evaluate, and operationalize experimentation frameworks for auction, pricing, and yield optimization. Building scalable methods to measure impact, validate model performance, and improve revenue driving decision systems. • Model Production & Monitoring: Productionize forecasting and optimization models by building backtesting, monitoring, and guardrail systems that ensure outputs are reliable, explainable, and safe to deploy in high output and delivery environments.

• Own the analytical function for the Discovery Mode ML squad, driving evaluation and continuous improvement of the models that power measurement and campaign optimization • Partner with ML engineers to develop evaluation frameworks and identify opportunities to improve model performance, reliability, and customer impact • Design and execute rigorous experiments to evaluate model quality, measure outcomes, and guide model development • Conduct deep-dive analyses to assess model performance and translate findings into clear, actionable recommendations for product and business stakeholders • Build, maintain, and evolve dashboards that track model health, customer metrics, and program performance • Collaborate with product managers, engineers, and cross-functional partners to align analytical priorities with squad goals and customer needs • Contribute to the broader Product Insights community by sharing best practices and helping raise the bar for analytics across Discovery Mode

• Drive the development of machine learning capabilities (forecasting, recommendation, ranking, optimization, or decision intelligence) powering customer-facing SaaS products. • Design reliable data and feature pipelines alongside models from discovery through experimentation, validation, deployment, and monitoring. • Partner with Product Managers and Software Engineers to embed ML directly into product workflows, user experiences, and decision-making tools. • Move quickly from prototype to production while balancing accuracy, interpretability, latency, maintainability, and business impact. • Define offline and online evaluation strategies, including model quality, drift, and reliability. Design A/B tests and causal measurement frameworks to prove ML features improve customer outcomes. • Collaborate with Data teams to ensure models are supported by high-quality features, while building feedback loops so product experiences improve over time. • Help manage and optimize cloud data infrastructure, ensuring trustworthy insights and proactively managing data health before it impacts users. • Bring strong judgment around when to use traditional ML, statistical modeling, LLMs, heuristics, or simpler product logic. Make practical trade-offs across model complexity and customer impact. • Clearly communicate what ML can and cannot solve to influence roadmap decisions, helping identify where machine learning can create true product differentiation. • Guide and mentor other data scientists, ML engineers, analysts, and cross-functional partners in applied ML best practices.