Senior Principal Data Scientist, CADD

Join our dynamic and innovative global Computer-Aided Drug Discovery (CADD) group, now expanding to India! Be a part of our Hyderabad team, where we unite diverse talents to revolutionize the validation and development of new targets in Biomedical Research. As a driving force behind drug discovery, we are excited to find an exceptional computational scientist like you to join our global ranks.
Imagine the opportunity to unlock hidden knowledge and disruptive insights from the vast and invaluable data collected by one of the worlds most renowned pharmaceutical companies. We need your expertise, experience, and unwavering passion to help us extract this wealth of information. Collaborating with a multidisciplinary group of scientists, you will be at the forefront of crafting inventive solutions to the most pressing drug discovery challenges, forging new paths toward groundbreaking medicines.
Are you ready to seize this extraordinary chance to make a significant impact in the field of drug discoveryWe invite you to embark on this thrilling journey with us, as we push the boundaries of whats possible in scientific exploration. Join our team and be part of a revolution that will shape the future of medicine.
Together, we will transform the landscape of drug discovery, accelerate breakthroughs, and change lives. Apply now and let your expertise shine in our dynamic and forward-thinking environment.

• Drive the design of medicinal chemistry efforts by applying in-depth knowledge of structure-activity relationships (SAR), a profound understanding of target biology, and predictive methods for assessing on- and off-target activity, physical properties, pharmacokinetics / pharmacodynamics (PK/PD), and synthetic feasibility.
• Thrive at the intersection of experimental and groundbreaking digital technologies, with a particular emphasis on expertise in machine learning and artificial intelligence (AI) as applied to small molecule drug discovery.
• Stay abreast of scientific literature and engage with internal and external scientists to incorporate biological insights into lead characterization and screening initiatives.
• Collaborate with interdisciplinary project teams to facilitate effective decision-making throughout the target identification to candidate nomination process. This involves applying and developing predictive models based on high-content and time-resolved screening data, including imaging techniques.
• Drive hypothesis generation to enhance clinical success rates for programs involving small molecules, peptides, RNAs, protein degradation, molecular glues, transient covalent inhibitors, and kinetic stabilization of drug-target complexes.
• Take a leading role in cross-disciplinary mechanistic studies using physics-based modeling and simulation, biophysical characterization, and cellular validation. These studies will inform the strategic targeting strategies of discovery projects, aiming for optimal mechanisms of action (MoAs).

• Advanced degree in medicinal chemistry, computational chemistry, computational biology, computational chemical biology, or a related field. Candidates with a laboratory-based background in chemistry and biology, supplemented with strong computational experience, are also encouraged to apply.
• 5+ years of experience working with project teams in a drug discovery environment.
• Proven track record of innovation through analogue design, leading to significant impact on discovery projects.
• Familiarity with drug design tools and high-performance computing environments and strong publication history in peer-reviewed journals.

• Proactively anticipates project needs with a clinical focus.
• Demonstrates rigor and diligence in idea substantiation, analogue design, and experimentation.
• Strong team orientation with multitasking and adaptability in support and leadership roles.
• Effective listener with excellent written and oral communication skills.
• Proficient in data visualization to effectively communicate insights.

Apache Hadoop, Applied Mathematics, Big Data, Curiosity, Data Governance, Data Literacy, Data Management, Data Quality, Data Science, Data Strategy, Data Visualization, Deep Learning, Machine Learning (Ml), Machine Learning Algorithms, Master Data Management, Proteomics, Python (Programming Language), R (Programming Language), Statistical Modeling