Job Description Summary

The Associate Director, Data Science will lead the design, development, and enterprise scale deployment of advanced AI and Generative AI solutions across P&O and Corporate Functions at Novartis. The role focuses on building intelligent, agentic, and knowledge augmented AI systems, applying unconventional problem-solving techniques, and shaping ambiguous challenges into clear end to end AI solutions that deliver measurable business value and strong enterprise adoption. The leader will deeply understand complex business problems across multiple stakeholders and domains, formulate integrated analytical and AI approaches to mine diverse data sources, apply advanced statistical and ML techniques to generate actionable insights, and solve business problems and also automate repetitive processes to drive efficiency at scale. This position demands strong foundations in statistics, machine learning, algorithmic thinking, solution design, and the full data science lifecycle. It also requires exceptional storytelling skills, smart visualization techniques, and the ability to see the big picture, translate analytical output into clear and impactful business narratives for senior leaders. The role needs an engineering driven mindset for MLOps, CI and CD, reusable AI asset creation, and scalable architectures, along with high agility to operate across diverse business domains. The ideal candidate embraces both low code and pro code paradigms, leverages productivity accelerators such as vibe coding, works confidently in demanding and fast paced environments, and demonstrates a continuous learning mindset. As a people leader, the Associate Director independently sets strategy, manages budgets and resources, coordinates complex multi team projects, and empowers teams through guidance, coaching, and supportive leadership with minimal oversight from senior management.

 

Job Description

Key Responsibilities

Location – Hyderabad #LI Hybrid

• Lead the end-to-end delivery of enterprise-wide AI and GenAI solutions, including problem framing, PoV execution, experimentation, model design, reusable asset creation, industrialization, and production deployment.
• Architect advanced agentic AI ecosystems such as multi actor orchestration frameworks, Agentic RAG systems, hybrid retrieval architectures, multimodal AI systems, fine tune LLM models when required, and interoperable AI agents using MCP or A2A standards.
• Build knowledge augmented generation systems using graph-based AI and Neo4j to enrich contextual reasoning and maximize the reuse of enterprise knowledge assets.
• Ensure alignment with enterprise data governance, lifecycle management, data quality, lineage, and AI compliance frameworks.
• Apply strong foundations in statistics, ML theory, algorithmic design, and unconventional problem solving to craft innovative AI solutions beyond standard approaches.
• Translate complex insights using clear storytelling, visualization, and business context for senior leaders and non-technical stakeholders.
• Apply strong engineering practices including Python development, modular coding, version control, CI or CD, MLOps, model monitoring, and observability frameworks.
• Partner with senior business leaders, architects, data teams, and technology stakeholders to align AI roadmaps with strategic priorities and accelerate adoption across functions.
• Proactively plan resources, manage risks, set accurate stakeholder expectations, and drive operational excellence in large scale AI programs.
• Define and track KPIs, adoption metrics, governance endorsements, and measurable business value for all deployed AI systems.
• Mentor and develop data scientists and engineers through hands on guidance, reviews, career support, and the creation of a psychologically safe and growth focused environment.

Essential Requirements:

• Minimum 10 years of experience in Data Science, AI, or Applied Machine Learning, with 3 years in Generative AI and LLM based solutions.

• Proven success leading enterprise scale AI programs with measurable outcomes.
• Expertise in LangGraph (and other agent orchestration frameworks), LangChain/Llama Index, Agentic RAG, graph-based AI, Neo4j, Azure AI, AWS, Databricks.
• Strong Python programming skills and deep learning expertise using PyTorch or TensorFlow.
• Experience with statistical analysis, ML algorithms, traditional ML workflows, and optimization.
• Familiarity with big data ecosystems like Apache Spark.
• Strong experience in MLOps, CI or CD, model monitoring, and AI observability.
• Exposure to vibe coding and modern productivity accelerators.

Desirable Requirements:

• Strategic thinking with an enterprise lens. Deep technical leadership and architecture capability.
• Creative problem solving and unconventional thinking. Excellence in cross functional collaboration and stakeholder management.
• Responsible AI and ethics orientation. Strong visualization and storytelling traits.

You’ll receive: You can find everything you need to know about our benefits and rewards in the Novartis Life Handbook. https://www.novartis.com/careers/benefits-rewards

Commitment to Diversity and Inclusion:  
Novartis is committed to building an outstanding, inclusive work environment and diverse teams' representative of the patients and communities we serve.  

Join our Novartis Network: If this role is not suitable to your experience or career goals but you wish to stay connected to hear more about Novartis and our career opportunities, join the Novartis Network here: 
https://talentnetwork.novartis.com/network

 

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, Nlp (Neuro-Linguistic Programming) And Genai, Organization Awareness, Pandas (Python), Python (Programming Language), R (Programming Language), Sql (Structured Query Language), Stakeholder Engagement, Statistical Analysis, Time Series Analysis