About Matthew Martz¶

My expertise is in building and leading teams of Data Scientists, Machine Learning Engineers, Data Engineers, and Data Product experts of diverse experience levels, distributed across cultural and knowledge bases, to drive innovation, product development, and cutting edge, actionable research. I have a proven track record of mentoring, growing, executing, and building strategy around ambitious goals across the Data Science technical platform to provide business value.

My background is in Quantitative Cell Biology and I have a PhD in Biochemistry and Molecular Biology. I spent much of my career researching novel cancer therapeutics through live cell imaging and machine learning. You can now find me developing and implementing machine learning platforms and algorithms for everything from optimization problems to precision medicine and medicinal AI. In recent years I have researched and built novel recommenders, 24/7 production machine learning solutions, and received patents for personalization solutions in the consumer sales space and chemical profile recommenders.

Most recently, I have been working in the Biotechnology space where I have developed the long-term strategy and vision for Data Science, led a growing team of Data Scientists to develop novel algorithms in the predictive modeling and Bioinformatics spaces, and accelerated biological discovery. Moreover, I designed and implemented novel algorithms to detect differential modes of action in both natural products and whole microbe systems, and engineered a proprietary online learning platform to serve as an artificial intelligence decision guidance system for our extensive microbial collection and proprietary product discovery platform (Genesis), a digital twin. I rebuilt research and enablement programs across domains for knowledge generation and product creation and delivery using cutting edge deep learning techniques on records, genomics, and omics data as the global lead for digital science discovery and delivery programs. Established, built, and implemented data governance and provenance at both the discovery and enterprise levels.

Find more about me here: About Matthew Martz

Currently

GLOBAL TRAITS DIGITAL SCIENCE LEAD, GLOBAL TRAITS DISCOVERY AND DELIVERY PROGRAM LEAD

BENCHLING RESEARCH PIPELINE TECHNICAL LEAD

Led Trait (gene, phenotype) Discovery and Delivery efforts within RD and IT Digital Science across the entire Syngenta global space

Created, built, and led Generative AI program to automate and optimize the delivery process for gene delivery processes to target specific traits (phenotypes) or gene expression objectives

Created and led the program to use multimodal modeling with structured and instructed data, and foundation LLMs for knowledge generation, product discovery and delivery pipeline performance guidance, regulatory audits, and prompt-based experimental design

Owned stack from data model, application landscape, and AI research initiatives from gene discovery to trait (phenotype) introgression including building of API integrations, MLOps, DevOPs workflows

Team comprised business (Analysts, Architects, Delivery Managers, Scrum Masters), IT (Developers, QA, MLOps, DevOPs), and science (SMEs, Product Owners, Wet Lab Researchers) domains - both domestic and distributed teams

Mentored junior team members with a focus on professional development and upskilling opportunities

Led Data Science efforts to research and deploy foundational Large Language Models (LLMs) for protein design to predict expression levels of various molecular biology constructs as a software workbench for bench researchers - work emphasized multimodal data and contextualization/fine tuning of foundation models

Built strategy and vision across discovery and delivery to streamline scientific application portfolio, create single source of truth data streams, and initiated foundational work to bring cutting edge ML/AI technologies to the scientific pipeline

Communicated strategy and work initiatives to secure funding and oversee resourcing of execution

Collaborated with stakeholders across research and business domains to ensure cooperative acceleration and growth

Previously

DIRECTOR OF DATA SCIENCE/ML/AI AND TECHNICAL LEAD/STRATEGIC VISION

Served as Director of Data Science/Machine Learning/Artificial Intelligence strategy and innovation initiatives.

Led a growing team of Data Scientists working to drive biological research and accelerate product development.

Team size ranged from 7 to over 20, across disciplines of Data and Product.

Developed and drove 4 year strategy and vision for Data Science, Data Engineering, Bioinformatics, and Data Product for entire organization.

Advocate and conduct learning around Data Science/ML/AI products and platforms both to internal teams, executive team and board members, and external partners and collaborators (customers).

Directly contributed to high impact publications and invited for speaking engagements.

Served as Data Science liaison across the company, communicating strategy, accomplishments, initiatives, and best practices. Developed the long-term Data Science strategy and vision, hiring plan, technological innovation pipeline, and overall project management for the department.

Developed a proprietary predictive artificial intelligence decision guidance system to serve as a core to our proprietary natural and whole microbe product discovery platform - GENESIS, a digital twin technology. This digital twin technology has already generated a large corpus of actionable research, product leads, and accelerated screening paradigms to put the best leads in the field. Excitingly, my team was able to harness GENESIS to generate cross-indication predictive models to drive and accelerate lead identification across the research platform.

Led Data Science contribution to external manuscripts and internal white papers for both research, Data Science, and SOPs.

Sat on a team of technical leaders that drove research initiatives across the company and served as a hive mind to solve challenges across domains. Identified and addressed gaps in research and Data Science.

Developed a novel algorithm to use interpretable machine learning model ensembles to traverse genomic annotations and drive mode of action discovery from small datasets. Designed and developed a predictive modeling platform to accelerate product discovery in indication screening. Designed and developed a cross-indication prediction platform to enable multi-target identification.

Two publications and multiple patentable IP technologies developed within first year.

Previously

DIRECTOR OF DATA SCIENCE/ML/AI AND PRINCIPAL, RESEARCH AND MACHINE LEARNING

Served as director of Data Science/Machine Learning/Artificial Intelligence strategy and innovation initiatives.

Created, built, deployed, then led the genomic AI program for disease prediction, novel mode of action identification, biomarker discovery, and understanding population variation from DNA to signaling pathways/omics.

Served as technical lead for the development and utilization of mixed effects models to explain the impact of environmental factors on phenotypic outcomes in the background of genomic models.

Led a growing team of Data Scientists working to drive biological research and accelerate product development.

Team size ranged from 7 to over 20, across disciplines of Data and Product.

Developed and drove 4 year strategy and vision for Data Science, Data Engineering, Bioinformatics, and Data Product for entire organization.

Onboarded artificial intelligence methodologies like Generative AI and Large Language Models for genomic information and protein variant creation.

Owned stack from data model, application landscape, and AI research initiatives across all of ML/AI and Data Science including building of API integrations, MLOps, DevOPs workflows

Advocate and conduct learning around Data Science/ML/AI products and platforms both to internal teams, executive team and board members, and external partners and collaborators (customers).

Directly contributed to high impact publications and invited for speaking engagements.

Developed a proprietary predictive artificial intelligence decision guidance system to serve as a core to our proprietary natural and whole microbe product discovery platform - GENESIS, a digital twin technology in part combining genomics and geospatial data modeling. This digital twin technology has already generated a large corpus of actionable research, product leads, and accelerated screening paradigms to put the best leads in the field. Excitingly, my team was able to harness GENESIS to generate cross-indication predictive models to drive and accelerate lead identification across the research platform.

Led Data Science contribution to external manuscripts and internal white papers for both research, Data Science, and SOPs.

Two publications and multiple patentable IP technologies developed within first year.

Previously

POSTDOCTORAL RESEARCH FELLOW (Biological Machine Learning and AI) - University of North Carolina, Lineberger Cancer Center

American Heart Association funded research fellow in AI-driven precision medicine

Identifying, modeling, and understanding noise in single cell signaling during stress responses focusing on utilization of live cell imaging and machine learning on big data

Built and ran Data Science and Data Engineering capabilities in the areas of computer vision, natural language processing, artificial intelligence, infrastructure, high performance computing, predictive modeling, and mathematical modeling

Built and maintained live cell imaging infrastructure, developed a suite of machine learning algorithms to understand noise in single cell signaling, wrote and secured two fellowships, directly contributed to high impact publications and invited for speaking engagements, and mentored several graduate students and postdoctoral fellows.

Extensive experience working with time series data from signal data from engineering of pipelines and early data processing to complex machine learning algorithm development and implementation for decision-making and novel hypothesis generation

Expertise

Experience

GLOBAL TRAITS DIGITAL SCIENCE LEAD, GLOBAL TRAITS DISCOVERY AND DELIVERY PROGRAM LEAD - Syngenta

8+ years of Data Science and Machine Learning Engineering Leadership and Strategy Development at the Director or VP level

11+ years solutioning ML/AI architecture

5 patents awarded or in final status for the development and application of novel applications of ML/AI

25+ Member teams across disciplines in data and product

21 years of production Software Engineering experience

17 years of production Data Science/ML/AI experience and leadership

17 years of rigorous scientific training in academic programs - healthcare and precision medicine

Industry proven leader in Data Science and Machine Learning Innovation across the AI landscape

Mutaku is a collection of writings across the spectrum of biomedical research, software engineering (Python), Machine Learning, and Data Science.

Writings and Perambulations