Genome Editing Pipeline Data Scientist

Bayer•Chesterfield, MO

3d•$94,480 - $141,720•Onsite

About The Position

At Bayer, we are seeking a Genome Editing Pipeline Data Scientist to build connected data systems and decision models that improve the efficiency, predictability, and transparency of the Crop Genome Editing pipeline. This role involves connecting Research Operations initiatives (data, workflow, automation, sample tracking, and analytics) to establish an end-to-end (E2E) modeling capability from design to outcomes, leveraging advanced analytics, AI/ML, and responsible GenAI. The position will enable data-driven decisions and operational modeling across lab, greenhouse, and field activities, translate workflows into implementable plans for data science and engineering partners, and accelerate learning cycles for genome editing. You will work within diverse, empowered teams to advance our mission of Health for All and Hunger for None.

Requirements

Experience building and deploying data pipelines, predictive models, and AI/ML solutions in a scientific, R&D, or operations environment.
Strong skills in data modeling, analytics, and visualization, with the ability to translate complex workflows into scalable, AI/ML‑ready data structures and decision tools.
Proficiency with modern data and analytics technologies (for example, Python or R, SQL, workflow orchestration, dashboards/BI tools, and cloud‑based data platforms).
Demonstrated ability to collaborate effectively with cross‑functional partners (scientists, engineers, IT, data science) and to influence without direct authority in a multidisciplinary setting.
Strong communication skills, including the ability to explain technical concepts to non‑technical stakeholders and to drive change management and user adoption of digital tools.
Proven capability to manage multiple initiatives in parallel, set priorities based on impact, and define and track clear success metrics.
Commitment to responsible and ethical use of data and AI, including attention to privacy, IP protection, bias/robustness, and human‑in‑the‑loop decision making.
Willingness to work in a regional role with stakeholders across lab, greenhouse, and field environments, and to engage regularly with diverse, empowered teams.

Responsibilities

Build end-to-end data connections across the genome editing pipeline to enable reliable, integrated analytics and decision models (design → execution → outcomes).
Develop, validate, and operationalize E2E predictive models and AI/ML solutions (including GenAI/LLMs where appropriate) to improve prioritization, throughput, and predictability.
Partner across genome editing, Research Operations, IT, data, and engineering to automate workflows, connect tools and systems, and improve operational efficiency.
Translate scientific and operational workflows into technical plans that enable AI/ML‑ready data, analytics, and decision support; define success metrics and feedback loops to quantify impact.
Create and maintain pipeline dashboards, KPIs, and recurring status reporting for leadership and project teams; clearly communicate pipeline performance and insights.
Integrate model outputs into day‑to‑day operations (for example, design recommendations, experiment scheduling, QC triage) in close partnership with scientists, bioinformaticians, and engineers.
Drive adoption of digital and AI tools by capturing user needs, documenting workflows, and enabling training and change management; implement responsible AI guardrails (privacy, IP, bias/robustness, human‑in‑the‑loop).
Provide technical mentorship and enablement (playbooks, templates, code review) to increase adoption of modeling and AI across the Genome Editing organization.