Job Description Summary
Join the Modeling & Simulation Data Science team within the Translational Medicine, Pharmacokinetic Sciences Unit to advance data-driven drug discovery. This role combines advanced data science with robust software engineering to transform large-scale experimental datasets into impactful insights and scalable digital solutions supporting drug discovery and development.
We are seeking for a talent to provide support at Biomedical Research India within Pharmacokinetic Sciences (PKS) Modeling & Simulation, focusing on lead identification and optimization in close collaboration with Novartis colleagues in the US and Switzerland, to discover and advance innovative methods addressing areas of high unmet medical need. The PKS department handles a high volume of experimental data across ADME, PK and PD domains. This role plays a critical part in leveraging these data through advanced analytics, machine learning, and software engineering to inform decision-making, accelerate lead optimization, and enhance reproducibility and scalability of scientific workflows.
Job Description
Major accountabilities:
- Serve as a trusted partner between Data & Digital (D&D) and PKS wet and dry lab teams to identify gaps and translate business needs into strategically aligned solutions within the D&D portfolio.
- Act as a data science representative in Integrated Drug Discovery (IDD) programs, providing scientific and strategic input using experimental and computational data.
- Design, build, and maintain scalable data pipelines, applications, and analytical workflows.
- Develop, deploy, and maintain machine learning models to uncover structure–property relationships and support decision-making.
- Apply statistical analysis and data mining techniques to derive insights from complex biological and chemical datasets.
- Write production-quality, maintainable code following software engineering best practices (testing, version control, documentation).
- Collaborate across cross-functional teams to integrate computational solutions into scientific workflows.
- Identify opportunities for automation, improved data usage, and development of in silico models and digital tools.
- Communicate findings clearly to diverse audiences and contribute to the adoption of data-driven approaches.
Minimum requirement
- PhD in life sciences, computational biology, cheminformatics, bioinformatics, or a related field, and 3-4 years (PhD) / 7-8 overall years of relevant work experience in drug discovery within biomedical or pharmaceutical research settings.
- Strong expertise in machine learning, statistics, and data science workflows applied to drug discovery.
- Proficiency in Python and/or R with solid software engineering practices.
- Experience designing and deploying production-grade data products or ML systems.
- Strong understanding of data visualization and exploratory analysis.
- Excellent communication skills and ability to translate complex concepts into actionable insights.
- Experience in pharmacokinetics (PK), ADME, or PK/PD modeling.
- Familiarity with modern application frameworks or front-end technologies (e.g., JavaScript, Svelte).
- Experience with advanced ML methods such as deep learning or generative models.
- Experience working with large-scale scientific datasets and data platforms.
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
