Summary

The role is part of the commercial Data and Analytics team at Novartis, focusing on understanding complex business problems from various stakeholders and business functions. It involves formulating integrated analytical approaches to mine data sources, employing statistical methods, machine learning algorithms, and AI to discover actionable insights and automate processes, thereby reducing effort and time for repeated use.

About the Role

Major accountabilities:

• Experience of manipulating and analyzing large high dimensionality unstructured datasets, drawing conclusions, defining recommended actions, and reporting results with a proven ability in the modelling of sophisticated datasets in applied business application domains
• Ability to work and communicate with senior stakeholders across Novartis teams and proven track record to drive strategic priorities at enterprise level in a matrix environment
• Ability to translate business requirements to problem statements and drive hypothesis generation to identify right data science problems to be solved
• Ability to deliver enterprise level scalable solutions along with proven capability in building and developing data science driven software products and understanding of software lifecycle management and agile product delivery

• Demonstrated experience with modern data science approaches, including unsupervised and supervised classification and regression algorithms such as k-means clustering, support vector machines, random forests, neural networks, and deep learning.
• Advanced knowledge and proven experience with standard data science languages: Python, Databricks, and Snowflake, along with familiarity with database systems (e.g., SQL, NoSQL, graph).
• Comfortable with version controlling (git etc.) and ML lifecycle management tools (MLflow), with knowledge of Kedro preferred.
• Understanding of statistics, econometrics, ML algorithms (random forest, GBM, clustering techniques, etc.), and basic understanding of optimization.
• Proficient in building Deep Learning pipelines (familiar with LSTM, Temporal CNN, and other DL-based time series techniques).
• Understanding of advanced optimization techniques - Particle Swarm, Bayesian Optimization, etc..
• Advanced knowledge of multi-cloud environments such as AWS, MS Azure, Databricks, along with understanding and familiarity with software development principles.
• Extensive experience in developing and scaling data science products at the enterprise level and agile product development methodologies.
• Understanding of algorithm design, development, optimization, scaling, and applications with an ability to create reusable data science assets as a service.
• Good understanding of at least one business area where data science is applied.
• Masters (or Bachelors from a top-tier university) in a quantitative discipline (e.g., Applied Mathematics, Computer Science, Bioinformatics, Epidemiology, Statistics, Decision Science, Quantitative Research, Econometrics, Computational Physics/Chemistry/Biology).
• Minimum 5 years of relevant experience in Data Science, with at least 3 years in Pharma/Healthcare. Cross-industry experience is highly preferred. Minimum 2 years of international experience working with global teams.

• Experience of manipulating and analyzing large high-dimensionality unstructured datasets, drawing conclusions, defining recommended actions, and reporting results.
• Ability to work and communicate with senior stakeholders across teams and a proven track record of driving strategic priorities at the enterprise level in a matrix environment.
• Ability to translate business requirements to problem statements and drive hypothesis generation to identify the right data science problems to be solved.
• Ability to deliver enterprise-level scalable solutions along with proven capability in building and developing data science-driven software products and understanding of software lifecycle management and agile product delivery.