AD, Data Science

Job Description Summary

• Understand complex and critical business problems from various stakeholders and business functions, formulate an integrated analytical approach to mine data sources, employ statistical methods and machine learning algorithms to contribute to solving unmet medical needs, discover actionable insights and automate the process for reducing effort and time for repeated use.

-Manage the definition, 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 be able 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 set strategy, manage budget, ensure appropriate staffing and coordinate projects within the area supervised.

-If managing a team: empowers the team and provides guidance and coaching, with limited guidance from more senior managers.

Major accountabilities

• Innovate by transforming the way to solve a problem using Effective data management, Data Science and Artificial Intelligence.
• Articulates solutions /recommendations to business users. Provides pathways to manage data effectively for analytical uses. Presents analytical content concisely and effectively to non-technical audiences and influences non-analytical business leaders to drive major strategic decisions basis analytical inputs
• Coordinates, prioritize and efficiently allocates the team resources to critical initiatives: plans resources proactively, anticipates and actively manages change, sets stakeholder expectations as required, identifies operational risks and enable the team to drives issues to resolution, balances multiple priorities and minimize surprise escalations
• Collaborates with internal stakeholders, external partners and Institutions and cross-functional teams to solve critical business problems, and propose operational efficiencies and innovative approaches.
• Proactively evaluates the need of technology and novel scientific software, visualization tools and new approaches to computation to increase the efficiency and quality of the Novartis data sciences practices
• Provides agile consulting, guidance and non-standard exploratory analysis for an unplanned urgent problem
• Independently identifies research articles and reproduce / apply methodology to Novartis business problems
• Publishes in peer-reviewed journals, helps to organize sessions at external professional conferences and contributes to cross-industry work streams in external relevant working group
• Makes right choices from a breadth of tools, data sources and analytical techniques to answer a wide range of critical business questions
• Ensures exemplary communication with all stakeholders including senior business leaders
• Contribute to the development of Novartis data management and data science capabilities.
• May lead a Team / Function or in-depth technical expertise in a scientific / technical field depending upon the career path (Manager/Individual contributor)
• Reporting of technical complaints / adverse events / special case scenarios related to Novartis products within 24 hours of receipt
• Distribution of marketing samples (where applicable)
• Ensure a healthy and safe workplace by complying with Novartis HSE and ISEC standards, implementing measures, providing training and resources, supporting employee well-being, investigating incidents, and collaborating with HSE teams to maintain a safe environment while adhering to quality, ethical, health, safety, and environmental requirements

Essential Requirements

• End-to-end GenAI/agentic systems engineering: Ability to design, prototype, deploy, evaluate, and monitor LLM applications using RAG, tools, orchestration, memory/context, and safeguards.
• Strong software engineering and production delivery: Python, APIs, testing, CI/CD, version control, cloud/enterprise infrastructure, security, observability, and maintainable system design.
• Scientific stakeholder translation and leadership: Ability to convert ambiguous biomedical research needs into practical AI solutions, partner across teams, drive adoption, and show measurable impact.

Skills Desired

Artificial Intelligence (AI), Biostatistics, Business Value Creation, Change Management, Curious Mindset, Data Governance, Data Literacy, Data Quality, Data Science, Data Visualization, Deep Learning, Graph Algorithms, Learning Agility, Machine Learning (ML), Machine Learning Algorithms, Python (Programming Language), Stakeholder Engagement, Statistical Analysis, Time Series Analysis