Senior Principal Data Scientist

Job Description Summary
– 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, discover actionable insights and automate process for reducing effort and time for repeated use. 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 useful lifecycle. High agility to work across various business domains. Integrate business presentations, smart visualization tools and contextual storytelling to translate findings back to business users with a clear impact. Independently manage budget, ensuring appropriate staffing and coordinating projects within the area. If managing a team: empowers the team and provides guidance and coaching, with initial guidance from more senior leaders supervised. This is usually their first people manager experience.

The Role

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

This role offers hybrid working, requiring 3 days per week or 12 days per month in our London Office.

Key Accountabilities:

• 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.

• Contribute to planning, execution, interpretation, validation and communication of innovative exploratory biomarker and/or AI analyses and algorithms, to facilitate internal decision making, and support submissions of candidate drug and associated companion diagnostics packages.

• 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 as well as discussions on white papers/regulatory policy.

• Perform hands-on analysis of integrated clinical, outcomes and high-dimensional, patient-level biomarker data from clinical trials and the real world (genomics, transcriptomics, proteomics, flow cytometry etc.) to generate fit-for-purpose evidence that is applied to decision making in drug development programs.

• Contribute to the scientific content of materials for internal decision boards/regulatory/submission documents: Briefing Books, decision criteria, trial design(s), responses to Health Authority questions.

• 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).

• More than 3 years experience in pre-clinical and clinical drug development with extensive exposure to clinical trials.

• Clinical, pharmacological, and therapeutic knowledge of at least one disease area.

• 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 modeling, longitudinal modeling, Bayesian methods, variable selection methods (e.g., lasso, elastic net, random forest), design of clinical trials.

• Familiarity with statistical and analytical methods for genetics and -omics data analysis and working knowledge of high dimensional biomarker platforms (e.g., next generation sequencing, transcriptomics, proteomics, flow cytometry, etc.).

• 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:
Helping people with disease and their families takes more than innovative science. It takes a community of smart, passionate people like you. Collaborating, supporting, and inspiring each other. Combining to achieve breakthroughs that change patients’ lives. Ready to create a brighter future together? : to Diversity & Inclusion:
Novartis is committed to building an outstanding, inclusive work environment and diverse team’s representative of the patients and communities we serve.

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Apache Hadoop, Applied Mathematics, Big Data, Curiosity, Data Governance, Data Literacy, Data Management, Data Quality, Data Science, Data Strategy, Data Visualization, Deep Learning, Machine Learning (Ml), Machine Learning Algorithms, Master Data Management, Proteomics, Python (Programming Language), R (Programming Language), Statistical Modeling#J-18808-Ljbffr