Senior Director, Data Science & Machine Learning

Codexis seeks an experienced and collaborative Senior Director, Data Science & Machine Learning to build, lead and be accountable for an integrated capability spanning data science, computational biology, statistical modeling, and machine learning. This leader will translate Codexis’ scientific and business priorities into a multi-year functional roadmap and apply advanced analytical methods to consequential scientific and operational questions across enzyme engineering, process development, RNA manufacturing, and future digital manufacturing initiatives.

The successful candidate will combine strategic judgment with technical depth. They will help define where data science and machine learning can create the greatest value, prioritize high-impact opportunities, guide method and model choices, and remain close enough to the work to provide hands-on technical directions when it matters. The role requires scientific curiosity, strong operational execution, clear communication, and the ability to influence across functions without relying on direct authority.

What You Will Contribute

Strategic and Functional Leadership

· Partner with scientific, technical, and business leaders to define and own Codexis’ data science and machine learning agenda, including a multi-year roadmap aligned with company and functional priorities

· Identify and prioritize the scientific and operational challenges where advanced analytics, machine learning, and AI can create meaningful business impact

· Be accountable for directing resources and  investments toward the highest-value opportunities across discovery, development, manufacturing, and platform innovation rather than technical advancement alone

· Build and lead a high-performing team of data scientists, bioinformaticians, and computational (bio)scientists establishing the structure, capabilities and operating model needed to scale the function

· Recruit, coach, and develop exceptional talent while fostering a culture of scientific rigor, innovation, collaboration, accountability, and continuous learning

Scientific and Technical Leadership

· Provide technical and strategic guidance on experimental and project design, statistical analysis, model development, data strategy, validation, deployment, and risk mitigation

· Develop and guide analytical approaches, predictive models, algorithms, and machine learning methods for complex biological, engineering, process-development, and manufacturing datasets

· Apply scientific and technical judgment to distinguish promising, scalable opportunities from technically interesting but lower-impact efforts

· Remain sufficiently hands-on to shape method choices, challenge assumptions, and directly support the organization’s highest-impact programs

Cross-Functional Collaboration and Influence

· Build durable partnerships across Research, Process Development, Manufacturing, Quality, Regulatory Affairs, Information Technology, and Business Operations

· Embed data science expertise into multidisciplinary programs and translate scientific and business needs into rigorous, executable analytical strategies

· Communicate technical findings, uncertainty, and data-driven recommendations clearly to scientific, operational, and business stakeholders

· Present project outcomes, portfolio insights, capability needs, and strategic recommendations to leadership teams and drive cross-functional initiatives through successful completion and adoption

Data, Digital, and Model Foundations

· Partner with IT to improve the accessibility, quality, integration, and usability of scientific and operational data

· Help shape the enterprise data infrastructure and modern analytical workflows needed to support a scalable data science, machine learning, and AI application roadmap

· Establish and promote best practices for data governance, data stewardship, scientific computing, reproducibility, model validation, model governance, documentation, and responsible use

· Advance fit-for-purpose analytics and visualization capabilities that enable timely, informed scientific and operational decisions

Innovation and External Engagement

· Monitor advances in machine learning, artificial intelligence, computational biology, and biotechnology

· Evaluate emerging technologies and lead focused proof-of-concept efforts to determine whether they can materially improve scientific productivity, development outcomes, or decision quality

· Translate successful methods into adopted, maintainable workflows in partnership with scientific, operational, and IT teams

· Represent Codexis in scientific collaborations, external partnerships, conferences, and industry forums as appropriate