You Will:

• Manage definition, execution, and delivery of all technical goals in the areas of data and ML engineering
• Create alignment across ML and data engineering; prioritize and direct urgency and importance of high-value problems following our roadmap
• Partner with engineering leadership during quarterly and yearly planning meetings, advocating for strategies and objectives required  to achieve key ML and data goals
• Be an external thought leader—taking part in customer-facing engagements and  internal & external tech due diligence to inform key priorities and goals for the ML and data teams
• Be an internal technology promoter and take an active part in strategic decision-making
• Act as an expert in trends and best practices  in ML and data to inspire innovation and adoption of the latest engineering practices
• Lead world-class teams of directors, managers, and engineers to support our growth and deliver key ML and data products
• Guide and foster growth by overseeing the organizational design structure for your teams
• Play an active role in supporting and improving our hiring practices, ensuring that we’re best positioned for our growth objectives
• Commit to advancing diversity, equity, and inclusion (DEI) in the engineering division of BenchSci

You Have:

• 8+ years of experience in a senior engineering management role at a Senior Director or VP level
• 10+ years of total experience leading large engineering teams (50+ members) across different functions and shipping data and/or software products at scale
• Deep knowledge and experience in DataOps, MLOps, modern data architecture,  cloud-based data, ML tech stacks, and ML advancements
• Experience with architectural designs, technology exploration, and innovation management
• Proven track record building large-scale data platforms in a data-heavy, B2B environment
• Experience with building ML-based solutions and  data pipelines for internal or commercial data products
• Demonstrated experience with managing priorities and implementing effective agile PDLC/SDLC practices at scale
• Ability to establish effective working partnerships across cross-functional teams in both technical and non-technical settings