Manager - Data Science

Job Description Summary

The Insights and Decision Science (IDS) team is dedicated to enabling improved decision making at Novartis by leveraging superior data to identify actionable insights that drive enhanced performance. We collaborate closely with the US business, bringing insights and challenging ideas to empower smarter, data-driven decision-making. The Predictive Analytics team, within IDS, plays a crucial role in enabling data driven decisions for predicting patient journey & HCP behavioural outcomes crucial for achieving commercial success of Novartis products.
This role is crucial in understanding complex and critical business problems from US business functions, formulate integrated analytical approach to mine data sources, employ engineering, statistical methods and machine learning algorithms to discover actionable insights, and automate process for reducing effort and time for repeated use.

 

Job Description

Location – Hyderabad  #LI Hybrid

Key Accountability

• Analyze large datasets from various sources (Claims, Sales, Promotions, etc.) to uncover insights and drive decision-making processes.
• Develop and implement sophisticated data analysis, machine learning, and deep learning models to interpret complex pharmaceutical data & provide solutions as an individual contributor to business questions and problems in the field of Predictive Modelling 
• Collaborate with cross-functional teams to integrate AI-driven solutions across various platforms and services.
• Foster collaboration with the team; mentor a small team of Associate Analysts/ Analysts/Senior Analysts in delivering projects. 
• Deliver through structured project management approach with appropriate documentation and communication throughout project delivery cycle. 
• Support in creation and maintenance of standard operating procedures (SOPs), quality checklists (QCs) that will enable excellent quality outputs within the function. 
• Support in design and experiments for predictive analytics to optimize patient journey & HCP behavioral outcomes.
• Contribute to the development and enhancement of an internal ML/AutoML platform to standardize data pipelines, automate model training, tuning, and evaluation, and accelerate experimentation across predictive analytics use cases.
• Build reusable ML components (feature pipelines, evaluation modules, model templates) that can be integrated into the platform to ensure consistency and scalability.
• Partner with Data Engineering and Platform teams to operationalize AutoML workflows, ensuring governance, model reproducibility, and quality controls.
• Support exploration of GenAI and LLM techniques to enhance feature engineering, synthetic data generation, and documentation automation within predictive analytics workflows.
• Comply with all internal functional operating procedures like time & KPIs tracking, and other internal systems and processes. 

 

Essential Requirements:

• Minimum of 5 years of experience in data science, with 2-3 years of experience in the pharmaceutical healthcare industry.

• Deep understanding of Machine Learning, Deep Learning algorithms, Foundational/ LLM models, Statistics, and Predictive Analytics

• Proficient in programming languages such as Python, PySpark, Databricks, R

• Experience in building and deploying scalable machine learning models (e.g. cloud-based services such as AWS and Azure)

• Hands‑on experience with AutoML frameworks (such as Azure AutoML, H2O AutoML, DataRobot, or custom ML automation pipelines).

• Strong understanding of ML platform components including model registries, feature stores, automated evaluation, and monitoring workflows.

• Exposure to GenAI/LLM‑based tools for improving analytics efficiency (e.g., automated feature suggestions, code generation, or summarization).

• Excellent problem-solving skills and ability to identify creative solutions to complex problems

• Strong ability to effectively communicate and work across different time zones

• Excellent verbal and written communication skills to articulate and present business problems and solutions to key stakeholders persuasively

Education (minimum/desirable): 

• Bachelor's degree in related field is required; Master of Science and/or MBA strongly preferred 

Additional qualifications are as follows: 

• Ability to balance operational execution with high-level strategic thinking, supporting both day-to-day performance and long-term business goals

• Ability to work collaboratively with cross-functional teams, including Marketing, Field & Sales, Data Enablement & Platform Teams for product development

• Excellent communication skills, capable of effectively collaborating with senior leadership

• Ability to thrive in a fast-paced, dynamic environment and adapt to changing business needs and priorities

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.  https://www.novartis.com/about/roadmap/people-and-culture 

Commitment 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. 

Values and Behaviors: Demonstrates and upholds Novartis values and behaviors in all aspects of work and collaboration. 

Location: Hyderabad NKC. Hybrid | 3 days a week in office is mandatory. 

 

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