Machine Learning Scientist, Frontier Research, AI for Drug Discovery (AIDD)

About the role

A healthier future. It's what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That's what makes us Roche.

Advances in AI, data, and computational sciences are transforming drug discovery and development. Roche's Research and Early Development organisations at Genentech (gRED) and Pharma (pRED) have demonstrated how these technologies accelerate R&D, leveraging data and novel computational models to drive impact. Seamless data sharing and access to models across gRED and pRED are essential to maximising these opportunities.

The new Computational Sciences Center of Excellence (CoE) is a strategic, unified group whose goal is to harness the transformative power of data and Artificial Intelligence (AI) to assist our scientists in both pRED and gRED to deliver more innovative and transformative medicines for patients worldwide.

The Opportunity

Within AI for Drug Discovery, the Frontier Research team advances fundamental machine learning and statistics methods, motivated by real-world challenges in drug discovery. In biology and medicine, many important problems cannot yet be addressed with off-the-shelf machine learning approaches-they demand not only novel solutions, but also new paradigms for framing and thinking about the problems themselves.

Instead of focusing on incremental improvements, we work on long-horizon ideas that require a creative and broad perspective, developing solutions at a level that can be instantiated for multiple internal and external applications. If you're excited about this kind of research, join us on an impactful journey of innovation at Prescient.

In this role, you will

• You will develop new methods and frameworks for uncertainty quantification, statistical inference, and experimental design, motivated by the diverse problem settings that arise when using machine learning to guide decision-making across the drug-discovery pipeline.
• You are expected to contribute to and drive research projects that result in conference and/or journal publications and open-source code, and that foster cross-organizational collaborations and applications.
• You are expected to engage actively in knowledge dissemination, presenting your work & expertise at internal and external scientific conferences.
• You will collaborate closely with our team in South San Francisco, New York, and Basel, Switzerland.

Who You Are

• Must be pursuing, or have completed within the last 12 months, a Ph.D. in Computer Science, Statistics, Applied Mathematics, Physics, Computational Biology, or related technical field, or have demonstrated equivalent research productivity.
• Demonstrated expertise in (any paradigm of) uncertainty quantification for decision-making, e.g., sequential/online statistical inference, experimental design, conformal prediction, reinforcement learning, calibration, game-theoretic statistics, etc.
• Proven publication record and experience contributing to the research community, including at conferences like NeurIPS, ICML, ICLR, AISTATS, UAI, etc., and/or relevant journals.
• Excellent skills in technical writing & communication, as well as collaboration and interpersonal skills.

Preferred

• Experience with developing machine learning and/or statistical methods for biological/clinical data is desired, but not necessary.
• Experience developing open-source packages preferred.

Benefits

Relocation benefits are NOT available for this job posting:

• The expected salary range for this position, based on the primary location of California, is $147,600 - 274,000.
• Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law.
• A discretionary annual bonus may be available based on individual and Company performance.
• This position also qualifies for the benefits detailed at the link provided below.

Benefits

• #ComputationCoE
• #tech4lifeComputationalScience
• #tech4lifeAI

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