Job Description Summary
- Understands complex and critical business problems from various stakeholders and functions, formulates integrated analytical approaches to mine data sources, employs statistical methods and machine learning algorithms to solve unmet medical needs, discover actionable insights, and automate processes to reduce effort and time for repeated tasks.
- Manages the definition, implementation, and adherence to the overall data lifecycle of enterprise data, from acquisition or creation through enrichment, consumption, retention, and retirement, ensuring the availability of clean, accurate data throughout its lifecycle.
- Demonstrates high agility to work across various business domains.
- Integrates business presentations, visualization tools, and storytelling to communicate findings effectively to business users, highlighting their impact.
- Independently sets strategy, manages budgets, ensures appropriate staffing, and coordinates projects within the area. If managing a team, empowers team members, providing guidance and coaching with limited oversight from senior managers.
Job Description
Our Development Team is guided by our purpose: to reimagine medicine to improve and extend people's lives.
We are optimizing our processes, investing in new technologies, and building capabilities in specific therapeutic areas and platforms to bring medicines to patients faster.
We seek talented individuals like you to join us and help give people with diseases and their families a brighter future.
Apply today and join a place where we thrive together!
The Role
As an Associate Director of Data Science in the Medical Affairs Advanced Quantitative Sciences group, you will discuss and implement data science methodologies applied to patient-level data (clinical, real-world, biomarker) across clinical development. You will combine your data science, AI skills, and scientific knowledge in biology or medicine to support drug development decisions in 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:
* Contribute to planning, execution, interpretation, validation, and communication of exploratory analyses and algorithms to facilitate decision-making.
* Provide technical expertise in data science and machine learning/AI to identify influencing opportunities and discuss white papers/regulatory policies.
* Perform hands-on analysis of integrated clinical trial and real-world data to generate evidence for decision-making in drug development.
Your Experience
* Ph.D. in data science, biostatistics, or a related quantitative field (or equivalent).
* Over 6 years of experience in clinical drug development with extensive clinical trial exposure.
* Strong knowledge of statistical methods like time-to-event analysis, machine learning, meta-analysis, mixed-effect modeling, Bayesian methods, variable selection techniques (e.g., lasso, elastic net, random forest), and clinical trial design.
* Proficiency in R and Python, with experience in data visualization, exploratory analysis, and predictive modeling.
* Excellent communication skills, both verbal and written.
* Ability to develop and deliver clear presentations for decision-making in internal and external settings.
Why Novartis: Helping people with diseases takes innovative science and a community of passionate, collaborative individuals. Join us to create a brighter future: https://www.novartis.com/about/strategy/people-and-culture
Commitment to Diversity & Inclusion: We are dedicated to building an inclusive, diverse work environment that reflects the communities we serve.
Join our Novartis Network: Not the right role? Sign up at https://talentnetwork.novartis.com/network to stay connected and learn about future opportunities.
Skills Desired
Apache Spark, Artificial Intelligence (AI), Big Data, Data Governance, Data Literacy, Data Management, Data Quality, Data Science, Data Strategy, Data Visualization, Machine Learning (ML), Master Data Management, Python, R, Statistical Analysis
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