Senior Principal Data Scientist Full-time

Senior Principal Data Scientist

Salary Range: £57,050.00 – £105,950.00

This role is based in London, UK. Novartis is unable to offer relocation support for this role. Please only apply if this location is accessible for you. As a Senior Principal Data Scientist in the Medical Affairs Advanced Quantitative Sciences group, you will be responsible for the discussion and implementation of data science methodologies applied to patient-level data (including various clinical, real-world, and biomarker data) across clinical development. You will combine your data science and AI skills and your scientific knowledge in biology or medicine or statistics to enrich drug development decisions in close collaboration with internal and external partners.

Major Accountabilities

• You will contribute to planning, execution, interpretation, validation and communication of innovative exploratory analyses and algorithms, to facilitate internal decision making
• You will provide technical expertise in data science and (predictive) machine learning/AI to identify opportunities for influencing internal decision making as well as discussions on white papers/regulatory policy.
• You will perform hands-on analysis of integrated data from clinical trials and the real world to generate fit-for-purpose evidence that is applied to decision making in drug development programs.
• Translate business questions into analytical problems and build solutions using visualization techniques, dashboards, machine and deep learning, and statistical modelling.
• Search and develop new algorithms, methods, statistical models and business models, providing insight into structured and unstructured data.
• Apply R-based analytical and statistical techniques to solve business problems
• Applying data cleaning, wrangling, and analysis techniques using tools like AI, R, Python and Pandas.
• Developing and managing data pipelines using tools like AI, R, Python, Bash, and SQL.

Essential Requirements

• Master's degree or Ph.D. in data science, biostatistics, or other quantitative field (or equivalent).
• Experience in clinical drug development with extensive exposure to clinical trials.
• Strong knowledge and understanding of statistical methods such as time to event analysis, machine learning, meta-analysis, mixed effect modeling, longitudinal modeling, Bayesian methods, variable selection methods (e.g., lasso, elastic net, random forest), design of clinical trials.
• Strong programming skills in R and Python. Demonstrated knowledge of data visualization, exploratory analysis, and predictive modeling.
• Excellent interpersonal and communication skills (verbal and writing)
• Ability to develop and deliver clear and concise presentations for both internal and external meetings in key decision-making situations.

At Novartis, we're committed to reimagining medicine together – and rewarding the people who make it happen.

Expected Annual Base Salary Range for role: £57,050.00 – £105,950.00

The base salary offered is determined based on gender-neutral objectives, such as relevant skills, competencies and experience in accordance with the Novartis pay setting policy and upon joining Novartis will be reviewed periodically.

In addition to your base salary, you may be eligible for a performance-based bonus depending on certain performance parameters.

The rewards of being part of our team go far beyond base pay and incentives. We also offer a variety of competitive benefits in kind to help you thrive personally and professionally, such as insurance plans, retirement plans, wellbeing resources and global recognition programs. In addition, we provide flexible and hybrid working options, where possible, and minimum 14 weeks paid parental leave.

Novartis is committed to building an outstanding, inclusive work environment and diverse teams' representative of the patients and communities we serve.

Skills Desired: Artificial Intelligence (AI), Biostatistics, Change Management, Curious Mindset, Data Governance, Data Literacy, Data Quality, Data Science, Data Visualization, Deep Learning, Graph Algorithms, Learning Agility, Machine Learning (ML), Machine Learning Algorithms, Python (Programming Language), Stakeholder Engagement, Statistical Analysis, Time Series Analysis