- Work with a team of AI researchers, data scientists and SMEs with core domain expertise to develop and deliver focused, robust, performant AI algorithms and solutions to accelerate drug discovery.

- Stay informed about the latest AI methods, increase understanding of the problem domain, and ask detailed questions to identify potential areas for innovation in AI across the drug discovery pipeline.

- Promote awareness of advancements in AI methods for addressing key research questions within biomedical research.

- Fluently adopt Engineering and Product Development resources to ensure the adoption of AI solutions.

- Help position AI-aided drug discovery contributions to deliver and support progress of BRs portfolio, enable new kinds of therapeutic discoveries, shorten cycle times, and increase efficiency.

Collaboration & partnership :

- A respectful, team-player attitude is an absolute must.

- Regularly communicate, engage, align with AICS teams, broader data science community, and senior scientists.

- Initiate and lead key high-value internal collaborations across BR.

- Help design translatable metrics for AI models that will lead to tangible impact in collaboration with BR DAs and FAs.

- Collaborate cross-functionally to translate model outputs into actionable hypotheses and guide experimental design.

What youll bring to the role :

- 5+ years of significant experience in innovation, development, deployment and continuous support of Machine Learning and modeling.

- Strong hands-on coding proficiency in Python and deep learning frameworks and an expertise in bringing advanced analytics insights and actions to a large research organization

- Wide exposure to representation learning, deep generative modeling, probabilistic reasoning, and explainability approaches. with a deep passion for understanding and explaining the technical concepts underlying ML approaches.

- Strong understanding and experience in using version control systems for developing software (e.g. GitHub, git, subversion, bitbucket, etc.) with expertise in large-scale model training, distributed computation, and foundation model adaptation.

- Publications, patents, or open-source contributions that demonstrate machine learning innovation and expertise, with passion for understanding emerging technologies and how they can integrate into business solutions

- Ability to balance requirements, manage expectations, and drive effective results using a proactive can-do attitude towards identifying and resolving issues, as well as a proven ability to work exceptionally well within complex matrixed teams.