VP, Data Science & AI

Deepcell’s cutting edge technologies are at the intersection of biomedical engineering, artificial intelligence, and single cell multiomics. We are a fast-growing, series B Stanford spin-off company that has developed a unique platform for use in research, diagnostic testing, and therapeutics. We combine microfluidics, imaging, deep learning, and genomics to identify, isolate and analyze live, single cells. Our technology addresses diverse applications in the life sciences. We are a small team of passionate innovators in biomedical engineering, artificial intelligence, molecular biology, and genomics. Our technology has won multiple prestigious awards and is backed by top-tier venture capitalists in Silicon Valley. Deepcell is an AI-powered platform that analyzes, classifies and isolates label-free, viable cells based on their visual features for use in basic and translational research across the life-sciences industry.
The role of VP, Data Science and AI is a core leadership position responsible for delivering best in class data science capabilities. Reporting directly to the CTO and cofounder, you will play a key role in managing and enhancing a data science team, developing advanced capabilities that unlock our company vision, and transforming experiences through data science techniques. You will be responsible for advancing data science and advanced analytics capabilities including defining the right organization, tools & technology, processes, and governance needed to meet the needs of our expanding business. This exciting role is responsible for working across Deepcell on the strategy, use case exploration, and deployment of AI driven offerings.

Responsibilities

• Design, own, and implement Deepcell’s data science strategy in line with the overall business objectives with extensive experience developing and deploying ML/AI enabled products at scale with significant experience in deep learning.
• Build a data science roadmap and help inform and co-create company’s overall roadmap.
• Build and direct a high-performing data science team, providing leadership, guidance, and mentorship to team members including skilled bioinformatic and computational data scientists and engineers.
• Review architecture and design to deploy predictive and generative models and algorithms that support product requirements. 
• Establish and maintain relationships with key stakeholders/data science advocates across the organization to ensure data science initiatives align with business goals, ROI and successful deployment of algorithms/data products.
• Lead the development of advanced analytical capabilities and product features with the ambition of creating long-term strategic data/analytics assets for the company.
• Build tools and solutions to measure model performance over time.
• Provide data governance on data science work (sandbox testing, artifacts to ensure the “right way to perform”) ensuring accuracy, security, and AI/ML ops execution excellence.
• Responsible for leading the development and deployment of cutting-edge machine learning models, data science tools, including incrementality testing.
• Foster collaborative innovation with key strategic partners that will be in a support role for key projects and capacity demand.
• Take initiative and stay up to date with the latest data science trends, techniques, and best practices, determining how to incorporate the most suitable practices in the department.

Qualifications

• Master's or Ph.D. degree in Data Science, Computer Science, Mathematics, Statistics, Bioinformatics or a related field.
• At least 10 years of experience in data science, with a proven track record of success in leading and managing a large cross functional team in the tech or biotech industry.
• Strong knowledge of data science such as Business Intelligence, Artificial Intelligence, Machine Learning, Advanced Analytics, Big Data, Data Management and Data Governance.
• Prior experience with development of production level deep learning models.
• Thorough understanding of the recent advances in the field especially around large models, unsupervised approaches, etc. 
• Thorough understanding of 3rd party solutions and strategic insights on do vs buy options. 
• Demonstrated leadership and project management skills in deploying, leading and implementing successful projects.
• Excellent leadership skills, with the ability to inspire and motivate a team to achieve ambitious goals.
• Expertise in architecting, designing and deploying predictive and generative models and algorithms in real-world business environments.
• Solid technical and in-depth knowledge of machine learning, statistical modeling, and programming languages such as Python or R.
• Excellent communication and interpersonal skills, with t