Senior Principal Data Scientist

Job Description Summary

Office Location: London (The Westworks), United Kingdom
#LI-Hybrid Hybrid (12 days per month on-site if living within 50 miles of our London office)
#LI-Remote Remote (if living beyond 50 miles of our London office)
In this role, you will understand complex and critical business problems, formulates integrated analytical approach to mine data sources, employ statistical methods and machine learning algorithms to contribute solving unmet medical needs in neuroscience, discover actionable insights and automate process for reducing effort and time for repeated use. Moreover, you will manage the implementation and adherence to the overall data lifecycle of enterprise data from data acquisition or creation through enrichment, consumption, retention, and retirement, enabling the availability of useful, clean, and accurate data throughout its lifecycle. You have proven record of working across various business domains and integrating presentations, smart visualization tools and contextual storytelling to translate findings back to business users with a clear impact.
Our Development Team is guided by our purpose: to reimagine medicine to improve and extend people’s lives.
To do this, we are optimizing and strengthening our processes and ways of working.
We are investing in new technologies and building specific therapeutic area and platform depth and capabilities – all to bring our medicines to patients even faster.
We are seeking key talent, like you, to join us and help give people with disease and their families a brighter future to look forward to.
Apply today and welcome to where we thrive together!

 

Job Description

As a Senior Principal Data Scientist in the Advanced Quantitative Scientists  group you will be responsible for the discussion and implementation of data science and high-dimensional modelling methodologies applied to patient-level data (including various biomarker, clinical and outcomes data) across clinical development in Neuroscience. You will combine your data science and AI skills and your scientific knowledge in biology, imaging or medicine to enrich drug development decisions in close collaboration with internal and external partners.

Key Accountabilities:

• You will provide global strategic data science leadership and support to clinical development programs of low to mid complexity, based on relevant technical and disease area knowledge.
• You will contribute to planning, execution, interpretation, validation and communication of innovative exploratory biomarker and/or AI analyses and algorithms, to facilitate internal decision making, support submissions of candidate drug and research collaborations.
• You will provide technical expertise in data science and (predictive) machine learning/AI as well as domain knowledge in biology and/or medicine to identify opportunities for influencing internal decision making and research collaborations.
• You will perform hands-on analysis of integrated clinical, imaging, digital, fluid biomarkers outcomes and high-dimensional, patient-level biomarker data from clinical trials and the real world to generate fit-for-purpose evidence that is applied to decision making in drug development programs.
• You will contribute to the scientific content of materials for internal decision boards/regulatory/submission documents (e.g. Briefing Books, decision criteria, trial design(s), responses to Health Authority questions) as well as for peer-reviewed publications.
• You will align with and influence the Analytics team (biometrician, pharmacometrician, data management, database programming, programming, medical and scientific writing) as well as cross-functional partners in research, regulatory, clinical and commercial teams on the biomarker and/or AI strategy, execution, and delivery of assigned projects.

Your Experience

• Ph.D. in data science, biostatistics, pharmacology, bioinformatics, mathematics, or other quantitative field (or equivalent).
• Experience in clinical drug development with extensive exposure to clinical trials.
• Clinical, pharmacological, and therapeutic knowledge in neuroscience. Good understanding of clinical study design principles and basic familiarity working with clinical data in a clinical trial (GxP) setting.
• Strong knowledge and understanding of (multivariate implementations of) statistical methods such as time to event analysis, machine learning, meta-analysis, mixed effect modelling, longitudinal modelling, Bayesian methods, variable selection methods (e.g., lasso, elastic net, random forest), design of clinical trials.
• Familiarity with statistical and analytical methods for high dimensional data (e.g. imaging, digital, genetics or -omics data).
• 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.

Why Novartis:
Our purpose is to reimagine medicine to improve and extend people’s lives and our vision is to become the most valued and trusted medicines company in the world. How can we achieve this? With our people. It is our associates that drive us each day to reach our ambitions. Be a part of this mission and join us! Learn more here: https://www.novartis.com/about/strategy/people-and-culture

You’ll receive:

You can find everything you need to know about our benefits and rewards in the Novartis Life Handbook.

https://www.novartis.com/careers/benefits-rewards

 

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, Logistic Regression Model, Machine Learning (ML), Machine Learning Algorithms, Nlp (Neuro-Linguistic Programming) And Genai, Pandas (Python), Python (Programming Language), R (Programming Language), Sql (Structured Query Language), Stakeholder Engagement, Statistical Analysis, Time Series Analysis