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Data Scientist - Machine Learning - Genome Modeling

Company

Bayer

Address Chesterfield, MO, United States
Employment type FULL_TIME
Salary
Category Chemical Manufacturing,Biotechnology Research,Pharmaceutical Manufacturing
Expires 2023-07-24
Posted at 11 months ago
Job Description
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.


Data Scientist - Machine Learning - Genome Modeling


Your Tasks And Responsibilities


The primary responsibilities of this role, Data Scientist - Machine Learning – Genome Modeling , are to:


  • Train and analyze AI techniques on industry s most extensive global agriculture dataset (i.e. genetic and phenotypic data) in the world, including highly controlled and real world data;
  • Implement Bayesian modeling frameworks on existing datasets/models and expand to more extensive data and improve Bayesian or AI/ML models;
  • Investigate the efficacy of gene regulatory networks to establish an interpretable representation tool for realizing new trait experiments and gene editing targets in corn and soy;
  • Be asked to lead or assist in several areas, such as: gather, curate, and quality control new data sources across Research and Development (R&D) teams;
  • Develop models for assembling genomes of high-throughput skim-seq for tens of thousands of crop genotypes;
  • Identify how complex genetic interactions lead to observed phenotypes in an efficient and interpretable manner and determine whether these insights lead to better design and predictions for our current and future intelligent pipelines;
  • Develop state of the art AI-assisted genetic discovery tools to uncover novel genetics with optimal phenotypic performance by leveraging advanced mathematical models and theoretically proven concepts of machine learning, Bayesian optimization, and/or graph theory;
  • Work on a team as an individual contributor with other Data Scientists and builds cross functional relationships between Crop Science, Pharma, and Consumer Health divisions to collaboratively partner and effectively network within the Data Science Community;
  • Assess feasibility and develop Artificial Intelligence/Machine Learning (AI/ML) for predicting phenotypes from high-resolution genetic inputs, including raw reads/k-mers to SNP markers in corn and soy;


Who You Are


Your success will be driven by your demonstration of our LIFE (Leadership Integrity Flexibility Efficiency) values . More specifically related to this position, Bayer seeks an incumbent who possesses the following:


Required Qualifications:


  • Ph.D. OR Master’s degree OR Bachelor’s degree;
  • Proficient at identifying trends in a dynamic environment and adapting strategies to deliver the business objectives.
  • Experience in one or more of the following areas: Machine/Deep Learning, Bayesian Statistics, Uncertainty Quantification, Computational Biology, Computer Science, Probability, Probabilistic modeling, Nonlinear Dynamics, Hierarchical models, Applied Mathematics, Engineering or other related quantitative discipline;
  • Ability to identify common goals, build trusting relationships with internal and external stakeholders, and drive solutions;


Preferred Qualifications:


  • Track record of published work in peer-reviewed journals;
  • Experience working in a cutting-edge research lab supporting cultural and technical diversity to effectively engage broad thinking and problem solving.
  • Six (6) or more years of relevant experience (including post undergraduate education & industrial experience);


Domestic relocation may be available for this role.


Visa Sponsorship may be available for this role.


Position may be based in Chesterfield, Missouri, Cambridge, Massachusetts, Boston, Massachusetts, or an approved Bayer Crop Science site globally.


Position may include opportunity to work up to 100% remote.


Employees can expect to be paid a salary between $100,000 to $151,000.  Additional compensation may include a bonus or commission (if relevant).  Additional benefits include health care, vision, dental, retirement, PTO, sick leave, etc. This salary (or salary range) is merely an estimate and may vary based on an applicant’s location, market data/ranges, an applicant’s skills and prior relevant experience, certain degrees and certifications, and other relevant factors.


YOUR APPLICATION


Bayer offers a wide variety of competitive compensation and benefits programs. If you meet the requirements of this unique opportunity, and want to impact our mission Science for a better life, we encourage you to apply now. Be part of something bigger. Be you. Be Bayer.


To all recruitment agencies: Bayer does not accept unsolicited third party resumes.


Bayer is an Equal Opportunity Employer/Disabled/Veterans


Bayer is committed to providing access and reasonable accommodations in its application process for individuals with disabilities and encourages applicants with disabilities to request any needed accommodation(s) using the contact information below.


IMPORTANT NOTE for POTENTIAL US CANDIDATES: Bayer expects its colleagues to be fully vaccinated against COVID-19. Bayer defines fully vaccinated in alignment with CDC which is two weeks after completing the two-dose vaccine regimen or two weeks after completing the one-dose regimen. Additionally, Bayer colleagues are also required to comply with state, local and customer requirements.


Location: United States : Missouri : Chesterfield || United States : Massachusetts : Boston || United States : Massachusetts : Cambridge || United States : Missouri : St. Louis


Division: Crop Science


Reference Code: 786809


Contact Us


Email: [email protected]