Role Overview

This is a hands-on leadership role requiring strong technical expertise in bioinformatics, experience managing scientific teams, and the ability to drive projects from concept to delivery. The ideal candidate enjoys solving complex scientific problems, mentoring people, and building a collaborative, high-performing team.

Key Responsibilities

Scientific & Technical Leadership

- Lead bioinformatics activities across drug discovery programs
- Define computational strategies and provide technical direction to the team
- Review scientific work and ensure technical quality
- Apply computational techniques such as Molecular Docking, Molecular Dynamics, FEP, QM/MM, DFT, QSAR, Pharmacophore Modelling, Virtual Screening, Structure- and Ligand-Based Drug Design
- Evaluate and introduce new computational methods and technologies where appropriate

Project Management

- Plan and manage multiple projects, ensuring timely delivery and high scientific standards
- Prioritize work, allocate resources, identify risks, and monitor project progress
- Work closely with cross-functional teams to achieve project goals

People Leadership

- Lead, mentor, and develop a team of computational scientists
- Set clear goals, provide regular feedback, and support career development
- Encourage independent thinking, collaboration, and accountability
- Foster a culture where team members continuously learn, challenge themselves, and look for better ways of solving scientific problems
- Build technical capability within the team through coaching and knowledge sharing

Cross-functional Collaboration

- Partner with Chemistry, Biology, Bioinformatics, and AI/ML teams
- Translate computational results into actionable scientific recommendations
- Present technical findings to internal and external stakeholders

Quality & Compliance

- Ensure scientific rigor, data integrity,



documentation, and compliance with company standards
- Promote best practices in computational workflows and responsible use of AI/ML models

Education & Experience

- PhD in Computational Chemistry, Bioinformatics, Molecular Modelling, Pharmaceutical Sciences, Chemistry, Physics, or related discipline
- 8 to 12 years of industrial R&D; experience in bioinformatics or computational drug discovery
- Demonstrated experience leading scientific teams and multidisciplinary projects
- Strong programming skills (Python preferred) and experience with scientific computing is an advantage

Technical Expertise Hands-on experience in several of the following:

- Molecular Docking
- Molecular Dynamics
- Free Energy Calculations (FEP)
- QM/MM and DFT
- QSAR
- Pharmacophore Modelling
- Structure and Ligand-Based Drug Design
- Virtual Screening
- Protein-Ligand Interactions
- Molecular Property Prediction
- AI/ML applications in computational drug discovery

Technical Skills and Experience

- Structure-Based Drug Design (SBDD): Expertise in protein structure modelling, molecular docking, binding-site characterization, proteinligand interaction analysis, and structure-guided hit identification and lead optimization.
- Ligand-Based Drug Design (LBDD) & SAR Analysis: Experience in deriving and interpreting structure–activity relationships (SAR) from experimental data using molecular descriptors, pharmacophore modelling,



quantitative structure–activity relationship (QSAR) modelling, and data-driven approaches to guide molecular design.
- Advanced Molecular Simulations & First-Principles Modelling: Proficiency in molecular dynamics (MD), enhanced sampling techniques, and binding free-energy calculations (FEP/TI, metadynamics, umbrella sampling) to characterize conformational dynamics and quantify binding thermodynamics. Experience with quantum mechanical methods (DFT, QM/MM) to investigate electronic structure, intermolecular interactions, polarization effects, and electron density-based characterization of protein–ligand interactions.
- Computational Molecular Design & DMTA Cycles: Ability to formulate mechanistic and data-driven molecular design hypotheses, propose novel scaffolds and analogues, and integrate computational predictions with experimental findings to drive iterative Design–Make–Test–Analyse (DMTA) cycles. Demonstrated experience in computational drug discovery campaigns for hit identification, hit-to-lead progression, and lead optimization, with proven capabilities as both an individual contributor and a team leader, driving scientific strategy, cross-functional collaboration, and project execution.

Leadership Competencies The successful candidate will demonstrate:

- Strong technical depth and scientific judgment
- Proven experience leading and developing technical teams
- Excellent problem-solving skills and the ability to think beyond conventional approaches
- A coaching mindset with a passion for mentoring and developing people
- Ability to balance hands-on technical involvement with delegation and team empowerment
- Strong communication, stakeholder management, and project execution skills
- A cooperative, results-oriented approach with a focus on continuous improvement

📌 Manager Bioinformatics (Bengaluru)
🏢 SRAVATHI ADVANCE PROCESS TECHNOLOGIES
📍 Bengaluru