# Senior Genome Editing Target Design Scientist

**Company**: Bayer Crop Science
**Location**: Chesterfield
**Work arrangement**: onsite
**Experience**: senior
**Job type**: full-time
**Salary**: $111,520.00 - $167,280.00
**Category**: Engineering
**Industry**: Manufacturing

**Apply**: https://talent.bayer.com/careers/job/562949976820203
**Canonical**: https://yubhub.co/jobs/job_445f055d-c72

## Description

At Bayer, we're driven to solve the world's toughest challenges and strive for a world where 'Health for all Hunger for none' is no longer a dream, but a real possibility. Our mission is to sustainably enhance agricultural productivity by seamlessly integrating gene editing and digital technologies, empowering farmers to meet the world's growing food demands while safeguarding the environment.

In this role, you will drive an impactful gene editing program by identifying gene editing targets and alleles that result in desired phenotypic impact, working with a diverse set of data inputs to develop genotype-to-phenotype predictions using some of the industry's most extensive and global agriculture and genetic datasets.

As a member of the Biology and Genome Design community, you will actively build your own acumen and capabilities while modeling best practices for others and partnering closely with cross-functional scientific, engineering, and IT teams across the company.

## Responsibilities

- Produce and document recommendations for alleles that deliver desired phenotypic impact, contributing to innovative gene editing strategies

- Develop and apply state-of-the-art genetic discovery tools to identify novel genetics with predicted optimal phenotypic performance by leveraging diverse genomic and phenotypic datasets

- Develop robust workflows and analysis pipelines that enable cross-team communication, data sharing, and decision-making, supporting iterative learning from key experiments to shape research direction

- Identify and refine methods for capturing complex genetic interactions and the influence of genetic variation on observed phenotypes to improve design and prediction for current and future gene editing pipelines

- Lead or assist in the gathering, curation, quality control, and analysis of new data sources (e.g., functional genomics, phenomics) across Research and Development (R&D) teams

- Collaborate closely with cross-functional and cross-cultural scientific, engineering, and IT partners to align gene editing and data science approaches with pipeline and business needs

- Influence key stakeholders by clearly communicating data-driven insights, challenges, and opportunities to facilitate solutions to complex scientific and technical problems

## Requirements

- PhD in Biological Sciences, Computational Biology, or a related field

- At least 3 years of post-PhD experience working in gene editing, functional genomics, or closely related fields

- Demonstrated ability to collect, analyze, and leverage complex biological datasets and translate them into testable hypotheses

- Experience with advanced computational and analytical tools in molecular biology, genetics, and biochemistry

- Distinct communication skills with fluency in English, both written and verbal

## Preferred Qualifications

- Educational preparation or applied experience in at least one of the following areas: Plant Biology, Genetics, Molecular Biology, Machine/Deep Learning, or other closely related discipline

- Demonstrated experience working collaboratively in cross-functional and cross-cultural teams to achieve common goals

- Ability to influence key stakeholders by articulating challenges and opportunities to drive solutions to complex scientific and organizational challenges

- Results-oriented mindset with demonstrated ability to prioritize and advance multiple projects at the enterprise level

## Skills

### Required
- genetic discovery tools
- gene editing
- functional genomics
- phenomics
- computational biology
- molecular biology
- genetics
- biochemistry
- data analysis
- data visualization

### Nice to have
- plant biology
- machine learning
- deep learning