Machine Learning Lead - Drug Discovery

Job Description

Vertex Pharmaceuticals is seeking a machine learning expert to lead machine learning efforts in the Data and Computational Sciences (DCS) team, which is responsible for the data science and computational science efforts for all of Global Research at Vertex.

I n this leadership role, the machine learning research fellow will lead the development and application of machine learning and artificial intelligence across applications in global research, preclinical, and pharmaceutical sciences to improve, advance, and accelerate the discovery and development of transformative small molecule medicines, cell therapies, and genetic therapies.

In this Director-level role, the fellow must possess extensive and comprehensive expertise in various machine learning methods, covering classical approaches (random forest, SVMs) and modern deep learning-based methods, with experience in translating theoretical algorithm development into practical and high performance implementations.

The fellow will have the opportunity to impact a variety of scientific areas include predictive models for small molecule compounds, protein structure prediction, lipid nanoparticle design, and cell therapy characterization and manufacturing, with the ultimate goal of improving or accelerating the research and development of transformative medicines for patients.

The initial focus of the role will be on small molecule drug discovery, including opportunities around predictive models and protein structure prediction.

The fellow will work collaboratively with scientific and data science experts across Vertex to identify, evaluate and implement modern methods for predictive models and generative models, and apply them to small molecule datasets.

The fellow will also drive method development to support active learning approaches in molecular design, reaction screening, and potentially other areas where iterative optimization is applicable.

The fellow will be a key member of the Data & Methods Leadership Team, which sets the strategy for our approach to foundational methods and capabilities for all of DCS.

They will also shape the future direction around use of Machine Learning and Artificial Intelligence for scientific applications, by evaluating opportunities across the global research, preclinical & pharmaceutical science projects at Vertex, and partnering with other data and technology leaders to define the infrastructure needed for those goals.

For success in this role, the fellow will need to be an independent thinker with a strong sense of ownership, excellent communication skills, and the capability to drive research ideas from first principles-based conceptualization to realization.

In addition to having the ability to contribute to engineering solutions when required, the fellow should be able to balance theoretical elegance of methods with practical considerations of implementation, to ensure that the methods are able to truly advance the discovery and development of transformative therapies.

Responsibilities :

Develop and lead machine learning methods for key scientific questions across global preclinical research & pharmaceutical sciences

Partner with scientific experts to evaluate and develop machine learning methods in various scientific domains, with an initial focus on partnering with chemistry and computational chemistry leaders to accelerate and enhance the discovery of small molecule medicines through improvements to the Design-Make-Test-Analyze cycle

Lead data integrity efforts to define guidelines that ensure that ML / AI models have appropriate governance and documentation processes

Evaluate and benchmark (constructing internal benchmark datasets, if necessary) machine-learning methods related to key scientific applications, such as small molecule property prediction and design, generative models and active learning

Modify existing methods, or implement novel methods to address project needs that are amenable to ML / AI approaches

Collaborate with software developers in Data Technology and Engineering and the ML / AI development group within DCS to transition novel methods into the production MLOps system

Drive strategic partnerships with other data science groups across the network.

Qualifications :

PhD / MS in computer science, statistics, or computational science, with a focus on machine learning methods, or a PhD in a related field (e.

g., computational chemistry or biology) with a strong emphasis in machine learning method development , and 10+ years of relevant experience.

Deep expertise in the mathematics & algorithms for machine learning, both classical as well as modern deep learning methods (transformers, RL, GNN), and active learning & model confidence methodologies

Excellent ability to strategically evaluate opportunities, communicate and collaborate with subject matter experts of diverse backgrounds, and rapidly learn about new scientific areas

Very strong Python programming skills and experience with modern machine learning tooling (scikit-learn, JAX, PyTorch, Tensorflow, etc.)

Experience in visualization and interpretation (xAI) of ML / AI models

Scientifically diverse, authoritative and effective communicator, with excellent verbal and written communication skills

Knowledge of concepts related to drug discovery and development

Pay Range : $188,000 - $282,000

$188,000 - $282,000

Disclosure Statement :