Manager, Forward Deployed Engineering and Data Science Data Science London

Are you passionate about revolutionising engineering with AI? Here at MonolithAI we're on a mission to empower engineers to use AI to solve even their most intractable physics problems. For example, developing next-gen EV batteries that charge faster and last longer. We experienced serious growth over the past year, and we have ambitious plans moving forward. It's an exciting time, and to continue our growth we are recruiting a Head of Forward-Deployed Engineering & Data Science.

You'll build and lead a world class Forward Deployed team (Forward Deployed Engineers and Forward Deployed Data Scientists) that ensures every customer realises full value from Monolith. You'll help and coach the team that translate ideas into production applications, ensuring every customer engagement is a success story. Your role is critical in driving AI adoption in some of the world largest engineering companies, whom we're lucky to call clients.

• Lead & scale a fully remote, customer facing forward-deployed team, setting clear goals, career paths, and a culture of high ownership.
• Own customer value: drive customer renewal and expansion by ensuring our AI applications deliver measurable ROI for every account.
• Own executive relationships: cultivate trusted relationships with our strategic customers while acting as their technical sounding board
• Champion customer obsession: instil a culture of agency, bias for action, and relentless focus on customer success.
• Close the loop: channel feedback into the product roadmap and ensure expansion opportunities are promptly flagged to Sales
• Build for scale: introduce playbooks, tooling, and processes that multiply impact without multiplying headcount.

• Proven leadership of customer facing technical teams (Engineering, DS), either at a consultancy firm or Enterprise SaaS company. Ideally you have experience with AI/ML products.
• Full stack: equally comfortable in C suite strategy sessions and technical deep dives.
• AI fluency: you can understand model drift, data quality gaps, and feature engineering - even if you're not coding daily.
• Bias for action & customer obsession: you measure your success in renewal, expansion, and advocacy metrics.
• Nice to haves: Python/SQL skills, engineering domain expertise (e.g., batteries, automotive)