Senior Director, Data Science & Machine Learning

Description

Codexis seeks an experienced and collaborative

Job Type

Full-time

Description

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

Requirements

Minimum Qualifications

• Degree in Machine Learning, Data Science, Computational Biology, Computer Science, Statistics, Engineering, or a related quantitative discipline; Ph.D. strongly preferred or equivalent record of scientific and technical accomplishment
• Typically, 12+ years of experience developing and applying data science, statistical, or machine learning methods to scientific or engineering problems, including significant experience leading highly technical teams and cross‑functional programs; exceptional candidate with an equivalent record of impact can be considered
• Demonstrated success building and guiding high‑performing teams, setting functional priorities, allocating resources, and leading complex, multidisciplinary programs in a dynamic environment
• Deep expertise in one or more areas of machine learning, statistical modeling, predictive analytics, data modeling, algorithm development, and quantitative interpretation
• Proficiency in Python and contemporary machine learning or scientific‑computing frameworks; familiar with R, SQL, or comparable analytical tools and data environments
• Ability to communicate complex technical concepts and recommendations clearly to scientific, technical, business, and executive audiences
• Proven ability to influence across the organization and without direct authority, establish cross‑functional alignment, and convert analytical insight into action

Preferred Qualifications

• Demonstrate experience applying quantitative, computational, or machine learning methods to life‑science, biotechnology, pharmaceuticals, or closely related scientific and engineering problems
• Experience with biological sequence or structure data, high‑throughput experimental data, process‑development data, or manufacturing datasets
• Experience with cloud‑based computing environments, modern data‑analysis workflows, model deployment, and large‑scale scientific computing
• Familiarity with foundation models, generative AI, and emerging approaches for scientific discovery and operational decision support
• Experience building a data science or machine learning organization and scaling its impact within a growing biotechnology company

What Success Looks Like

The successful candidate will establish Data Science and Machine Learning as a trusted, high‑impact capability across Codexis. They will define and execute a multi‑year roadmap, develop a strong multidisciplinary team, focus resources on the company’s most important scientific and operational questions, and deliver measurable improvements in discovery, development, manufacturing, and decision‑making. Over time, the function will combine robust data foundations, rigorous analytical methods, and practical adoption to help position Codexis as a leader in applying AI and machine learning to enzyme engineering, RNA manufacturing, and next‑generation bioprocess innovation.

Salary Description

$275,000 - $295,000 per year