Director, Principal Machine Learning Scientist

Bayer CropScience Ltd•Cambridge, MA

31d•$182,338 - $273,506

About The Position

At Bayer we're visionaries, driven to solve the world's toughest challenges and striving for a world where 'Health for all Hunger for none' is no longer a dream, but a real possibility. We're doing it with energy, curiosity and sheer dedication, always learning from unique perspectives of those around us, expanding our thinking, growing our capabilities and redefining 'impossible'. There are so many reasons to join us. If you're hungry to build a varied and meaningful career in a community of brilliant and diverse minds to make a real difference, there's only one choice. Director, Principal Machine Learning Scientist Meeting some of the most pressing healthcare and sustainability needs of our time, at Bayer we leverage the most advanced technologies to accelerate the discovery of new medicines. The Machine Learning Research team is seeking a passionate research scientist who will lead the development of innovative machine learning solutions for proteomic data analysis together with our R&D partners.

Requirements

Ph.D. degree in Computational Chemistry/Biology, Chem/Bioinformatics, Chemical/Biological/Molecular Engineering, or a related field at the intersection of life sciences and computer science;
Experience in omics data analysis, including development and evaluation of machine learning solutions to extract actionable insights from large, multiparametric omics datasets;
Excellent programming skills in Python;
Demonstrated expertise with data science, machine and deep learning libraries (e.g., PyTorch, JAX, scikit-learn), including relevant chemistry/biology libraries and software tools (e.g., RDkit, OEChem TK, Biopython).

Nice To Haves

8+ years of relevant post-PhD experience, including 4+ years in industry;
Experience with machine learning solutions for proteomic and/or chemo-proteomic data analysis;
Experience in drug discovery projects in target characterization, mode of action deconvolution, small-molecule hit generation, hit-to-lead, and/or lead optimization;
Familiarity with molecular modeling and design software (e.g., Maestro, LiveDesign, MOE, Discovery Studio, Flare, Pipeline Pilot);
Strong record of publications or patents related to data science solutions for chemistry and/or biology.

Responsibilities

Identify opportunities to enhance R&D's productivity and further our portfolio goals, across therapeutic areas, by leveraging proteomic and chemo-proteomic data with novel machine learning solutions;
Propose and catalyze the co-creation and implementation of interdisciplinary, machine learning solutions in support of target characterization, mode of action deconvolution, hit identification, and small-molecule design;
Collaborate with our experimental and computational R&D partners at Bayer and arm-lengths companies to prioritize, develop and ultimately leverage data science capabilities in support of our pipeline;
Work cross-functionally with data science, computational, and IT functions to deploy, scale-up, and operationalize relevant data science solutions;
Keep up to date with the latest advances in (chemo)proteomics data processing and analysis, and machine learning algorithms for protein-ligand structure and property prediction;
Contribute to the broader scientific community through publications, open-source projects, and conference presentations;
Apply an analytical, data-driven mindset to addressing business needs and goals, and nurture the self-development of leadership traits (including acting as a mentor and coach, guiding teams toward business outcomes, facilitating company-wide connections and collaboration, and contributing to value creating systems that leverage the passion and energy of our people).