NovartisSr. Specialist DDIT PO&CF Data Science & AI
AI in this role
Senior Specialist in Data Science & AI developing and deploying advanced AI, ML, and Generative AI solutions at Novartis.
Job Description Summary
The Senior Specialist, Data Science will support the development and deployment of advanced AI and Generative AI solutions across P&O and Corporate Functions at Novartis. The role focuses on implementing agentic and knowledge augmented AI components, contributing to end-to-end solution development, and applying strong analytical and machine learning skills to solve complex business problems. The specialist will work on data preparation, traditional ML/DL model development and Gen AI model-based development, evaluation, and integration of AI capabilities into enterprise workflows, while ensuring adherence to Responsible AI and data governance standards.This position requires solid foundations in statistics, machine learning, algorithmic thinking, and the data science lifecycle. It also requires the ability to translate analytical findings into clear and concise insights, applying visualization and storytelling techniques for business users. The ideal candidate brings hands on experience with Python, ML frameworks, LLM tooling, MLOps practices, and demonstrates curiosity, adaptability, and continuous learning. The Senior Specialist works collaboratively with cross functional teams, contributes to reusable AI assets, and supports solution implementation under the guidance of senior team members.
Job Description
Key Responsibilities
Location – Hyderabad #LI Hybrid
- Contribute to the development of AI and Generative AI solutions for Corporate Functions and P&O by supporting experimentation, feature engineering, prompt development, model training, and evaluation.
- Implement components of agentic AI architectures such as retrieval workflows, multimodal pipelines, and knowledge augmented models using enterprise approved tools and frameworks.
- Quickly learn and apply AI tools, agentic AI frameworks, and advanced GenAI solution patterns needed to solve a wide range of business problems.
- Support data preparation, enrichment, data quality checks, and alignment with enterprise data lifecycle practices including consumption, retention, and retirement.
- Develop analytical outputs, dashboards, and model insights using strong visualization and communication skills.
- Collaborate with business stakeholders to understand requirements and translate them into actionable analytical tasks and model enhancements.
- Apply core statistical and machine learning methods, test hypotheses, and participate in improving the performance and reliability of deployed models.
- Work closely with senior data scientists and AI engineers to implement scalable and reusable components, follow MLOps practices, and ensure compliance with Responsible AI principles.
- Document work clearly and contribute to shared repositories, reusable assets, and internal best practices.
- Demonstrate adaptability and continuous upskilling in new AI technologies, low code tools, and productivity accelerators.
Essential Requirements:
- 7+ years of experience in Data Science & applied ML.
- Minimum 2-3 years in Gen AI application development and delivery.
- Hands on experience with Python, ML libraries, data processing frameworks, and Generative AI tools.
- Experience applying machine learning algorithms and analytical methods to real world business problems.
- Familiarity with LLMs, prompt design, embeddings, and retrieval components.
- Good understanding of data governance, data quality, and data lifecycle fundamentals.
- Experience with version control, testing, and basic MLOps workflows is preferred.
- Hands on experience with graph databases such as Neo4j and graph algorithms.
Desirable Requirement:
- Knowledge of vector databases.
- Strong grounding in traditional ML and deep learning algorithms.
- Change management awareness.
- Strong stakeholder engagement capability.
- High learning agility and curiosity.
- "Ready for anything" mindset is a must have.
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, Change Management, Curious Mindset, Data Governance, Data Literacy, Data Quality, Data Science, Data Visualization, Deep Learning, Graph Algorithms, Learning Agility, Logistic Regression Model (Inactive), Machine Learning (ML), Machine Learning Algorithms, Nlp (Neuro-Linguistic Programming) And Genai (Inactive), Pandas (Python) (Inactive), Python (Programming Language), R Programming, Stakeholder Engagement, Statistical Analysis, Structured Query Language (SQL), Time Series AnalysisHow we rate this
Sr. Specialist DDIT PO&CF Data Science & AI at Novartis rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- How do you structure and test a prompt to get consistent output from a language model?
- How do you monitor a model once it's live, and how do you know it needs retraining?
- What NLP problem have you worked on, and how did you measure whether it actually worked?
- How do you think about the risk of an AI system in this kind of role failing silently?
- Tell me about a project where machine learning was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: Prompt Engineering, ML Ops, NLP, AI Safety, and Machine Learning. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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