• Set vision & goals for overall science & technology platform development.• Responsible for the commercial team, including product selection, strategic partner development, techno-economic analysis, and marketing.• Guide R&D teams to execute on product opportunities.• Leadership of Data Science team, supporting development and application of advanced AI tools to accelerate biological engineering.

• Led a cross-functional team of ~20 scientists, engineers, and commercial development staff, including data scientists, molecular biologists, analytical biochemists, and bioprocess engineers to achieve aggressive synthetic biology product targets.• Responsible for the commercial team, including product selection, strategic partner development, techno-economic analysis, and marketing.• Served as technology visionary and evangelist, and public advocate for transformative technology platform. Worked with commercial team to develop strong partner relationships based on demonstrated ability to deliver new & improved microbial genome engineering solutions.• Responsible for portfolio and project management team, ensuring rigorous selection of attractive product opportunities and effective execution against project goals, from product concept to development and eventual commercialization.• Continued to lead the development of the GenoScaler™ rapid, massively parallel CRISPR based genome engineering platform, from its acquisition by Infinome, to radically reduce the time, cost, and risk of developing synthetic biology products. The platform consists of a fully integrated technology stack across the entire Design, Build, Test, Learn cycle. The Learn and Design components include high-throughput phenotype and genotype analysis systems as well as extensive use of statistical modeling and GPT type large language models for protein design.• Technology sets include Python, R, Java, AWS, Bayesian and classical statistics, Machine Learning, Deep Learning, Pytorch & TensorFlow, SQL, Django

• Led the successful spin-out of synthetic biology applications subsidiary, resulting in the creation of the world's first LeanBioengineering™ platform. This platform is based on Inscripta's core genome editing technology and has demonstrated transformational capabilities in the field of rapid genome engineering.• As CEO led all corporate-level functions and responsible for a wide range of tasks. These included investor relations, partner development, product selection, technology platform development, and execution. Leadership and expertise in these areas helped to ensure the success of the company and subsequent acquisition by Inscripta.• As CTO spearheaded the development of high-throughput data science and informatics systems to enable the rapid and efficient genome engineering programs necessary for the GenoScaler™ rapid genome engineering platform. These systems have proven to be a crucial component of Lean Bioengineering™, allowing for the rapid and accurate analysis of large scale HTP phenotype & genotype data. Technologies included development of custom Python based LIMS systems and application of AI, deep learning models for prediction and design of protein sequence performance.

• Executive Director of Data Science and Application Development lead, responsible for team of Data Scientists, Synthetic Biologists, and Analytical Biochemists developing a next-generation CRISPR based genome editing platform.• Led the determination of technology requirements, customer and key opinion leader engagement, and internal applications selection and execution.• Responsible for identifying analytical and computational opportunities and leading efforts to formulate and implement solutions.• Delivered statistical and other data science-based solutions to enable Design and Learn functions, including analysis of complex, high-dimensional data and implementation of machine learning-driven predictive and prescriptive modeling using state-of-the-art methods in R and Python.• Articulate and evangelize diverse genome editing technology applications, including forward and reverse engineering of proteins, pathways, and genomes.• Created content and presentations for colleagues, executive leadership, board, and investors to drive understanding and adoption of data science-driven strategies and capabilities for genome engineering.

• Enterprise data science group lead, providing support for all R&D units across the company. • Collaborated with leaders and research partners throughout the organization to organize and execute data and machine intelligence-driven discovery and development efforts.• Worked with lab scientists to identify, develop, and apply new synthetic biology and genome engineering tools.• Evangelized the use of high-dimensional predictive and prescriptive modeling techniques to enable rapid synthetic biological optimization of proteins, pathways, and genomes.• Recognized as a thought leader in machine learning-directed approaches to evolutionary and combinatorial system optimization.• Pioneered a culture of data science-guided experimental best practices, including extensive training and education of scientists and leaders on statistical analysis and design methodologies to extract fuller value from experiments and processes.• Evaluated and performed due diligence on technologies in the informatics and synthetic biology spaces to determine fit and value for the organization.• Worked closely with IT and software teams to identify and develop informatics solutions to enable an effective data science competency.

• Head of Data Science, responsible for a team of 30+ data scientists, computational biologists, and software engineers, reported to head of 400+ member Data Science and Informatics division.• Collaborated closely with partners across the organization to identify important research questions and opportunities. Designed small and large-scale experiments, and analyzed resulting datasets to derive actionable knowledge that advances transgenic discovery and product development.• Codified and articulated the results, mission, and vision of data science efforts to all levels within the organization, from tactical working groups to executive-level leadership.• Established Predictive Analytics Center of Excellence to harmonize and leverage data science efforts across R&D.• Accountable for enabling analytical rigor, peer review, shared learning, and technology development. Led groups of data scientists and stakeholders to identify and solve challenging, relevant problems in novel ways using state-of-the-art methods in machine intelligence.• Extensive experience analyzing high-dimensional, high-complexity biological datasets, including genomics, transcriptomics, metabolomics, hyperspectral, and other high-dimensional phenotypes. • Advanced practitioner and developer of a wide variety of analytical methods, including classical/ frequentist statistics, design of experiments, Bayesian statistics, resampling methods, modeling and simulation, causal network reconstruction, and machine learning (PLS, SVM, random forest, deep learning).• Experienced user of R, MATLAB, Java, SQL, and Python. Proficiency in C, C++, HTML, JavaScript, Delphi, and Mathematica.• Collaborated with data integration and warehousing efforts to extract, manage, and analyze research data enabling decision support and mining objectives.• Recognized expert in the field of machine learning-guided molecular evolution and scientific advisor for the company's Protein Engineering Center of Excellence.

• Promoted best practices in core technology and encouraged continuous innovation in the field of protein engineering through close collaboration with colleagues throughout all of R&D.• Utilized classical statistical methods extensively and created novel, modern (Bayesian) approaches for analyzing and modeling experimental data. Recognized as a statistical thought leader within the company.• Investigated new strategies for enzyme and strain engineering and experimental designs using extensive computer simulations.• Advocated for the use of novel bioinformatics methods and modern machine learning techniques for directed molecular evolution and strain engineering.

• Managed a newly formed competency group within the Systems Biology department, consisting of 10 computational biologists and software engineers.• Developed the group to act as the central point for all R&D data systems. Established close ties and communication channels with other competency groups, such as Molecular Biology, Analytical Biochemistry, and Cellular Engineering, as well as remote sites in Singapore and Hungary, to identify scientific software needs and develop or procure solutions as needed.• Conducted next-generation sequencing assembly and analysis to support strain analysis and engineering projects.• Developed software tools and novel methods for the statistical analysis and modeling of complex, high-dimensional datasets in support of strain and protein engineering projects.

• Led a synthetic biology innovation team tasked with developing the next generation protein engineering platform. Pioneered use of custom-developed algorithms and software along with high-throughput, automated molecular biology, to develop an Automated Parallel Synthesis (APS) platform. • In conjunction with statistical modeling and exploration of sequence-function space, transformed Codexis’ approach to directed evolution. This led to substantial gains in speed, efficiency, and protein performance improvements. The new technology platform and its resulting applications played a prominent role in Codexis’ successful IPO roadshow and valuation.• Developed high-dimensional statistical models to efficiently search sequence-function space and engineer improved protein properties.• Architect of a custom J2EE-based LIMS system and technical lead of seven developers. The system is used throughout R&D to store and analyze the majority of all strain and enzyme-related data and metadata.• Continually worked in concert with laboratory scientists to identify needs or discover new opportunities to develop custom algorithms or software applications in direct support of research goals.• Applied classical and modern statistical analyses for the design, analysis, and modeling of laboratory data.

• Served as the chief architect of a Java/J2EE based application framework used to develop both internal and external web-based applications.• Developed enterprise software applications for various clients, including Cisco and several startups.

• Created a web-based system in Java to organize, present, analyze, and interpret large amounts of chemo-genomic information.

• Developed enterprise software applications for various clients, including Cisco and several startups. Developed client-server and web-based software applications for small and Fortune 500 clients, including Intel, Mobil, and Oracle.

• Quantitative analyst for US Navy.• Obtained Top Secret clearance to work on highly classified projects for national security.