Deployment Strategy Principal (Life Science)

WovenLight is creating a new category of investment firm.

 

We combine traditional private equity investing with deep capabilities in AI and machine learning — in order to drive outsized value creation at our portfolio companies.

To execute our mission we are building an integrated team of world class data, investment, technology and operations professionals.

The Role

Deployment Strategy Principals are commercially focused leaders who combine deep understanding of life science businesses with genuine passion for AI and ML-driven value creation. You will lead WovenLight teams from a business and commercial perspective across two types of projects:

• Diagnostic projects — evaluating the value creation potential at target or portfolio companies, including identifying where AI/data science can unlock performance in life science contexts
• Deployment projects — executing on identified opportunities using WovenLight's deep AI/ML capabilities inside portfolio companies

The role is fast-moving and context-dependent. Across all projects, Deployment Strategy Principals are expected to lead on:

• Scoping and leading successful projects — both pre-acquisition diagnostics and post-acquisition execution
• Stakeholder management and communications — across multiple levels within a portfolio company and with one or more PE sponsors
• Leading change management activities at portfolio companies — overcoming the typical last-mile challenges of user education and adoption, which are particularly acute in regulated life science environments
• Supporting portfolio companies with broader analytics and AI topics — including organisational and operating model decisions specific to life science
• Tracking the impact of WovenLight's work and attributing clear financial value creation

Examples of projects you could work on include:

• Helping a pharma or medtech company position for sale by creating a roadmap of future AI/data value creation opportunities (sell-side diagnostic)
• Leading rapid evaluation of ML/AI value creation potential at a life science target (buy-side diagnostic)
• Predictive modelling to anticipate and avoid manufacturing downtime in a pharma production environment
• Demand forecasting and supply chain optimisation across complex product portfolios
• Identifying next best action for commercial or medical affairs teams
• Patient or customer churn modelling in healthcare services businesses
• Geospatial or network modelling for clinic, lab, or distribution footprint optimisation

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What We're Looking For

Life Science Domain Expertise

• Significant experience working in or advising life science businesses — including pharmaceutical, biotech, medtech, healthcare services, or diagnostics
• Strong understanding of how value is created across the life science value chain: R&D, clinical operations, manufacturing, commercial, and market access
• Familiarity with the regulatory, data governance, and compliance environment in which life science companies operate, and the implications for AI/data deployments

Business Leadership & Stakeholder Management

• Proven experience leading commercial transformations (4+ years) with a strong track record of driving adoption of new technologies or processes
• Exceptional stakeholder management skills, including experience educating senior leadership on the value of data and analytics initiatives and winning hearts and minds across organisations
• Change management expertise with a demonstrated ability to drive organisational adoption — in environments where culture and compliance constraints can slow progress
• Business acumen gained as an Engagement Manager at a consulting firm, Product Manager at a technology company, or in a similar commercially focused role

Commercial & Financial Acumen

• Strong understanding of the commercial and financial drivers of life science businesses, enabling you to identify and articulate value creation opportunities
• Financial literacy including familiarity with investing and corporate finance principles, so you can work effectively with PE professionals and quantify the financial impact of AI/data initiatives
• Ability to measure, track, and communicate the business impact of analytics and AI deployments — including clear attribution of financial value created

Technical (ML, AI, Software Development) Understanding

This is not a hands-on technical role, but Deployment Straetgy Principals must have a strong, practical understanding of key AI and ML concepts — enough to guide technical teams, evaluate opportunities, and ensure business impact. As part of our selection process, we will expect candidates to be able to answer questions such as:

• What is meant by a "data pipeline"?
• What's the difference between a Data Engineer and a Data Scientist in the context of an ML development and deployment workflow?
• What is meant by "overfitting" in a model?
• What is a "feature" in machine learning? What is feature engineering?
• What is an A/B test used for in analytics or ML?
• How does a generative AI project using a pre-trained model differ from a traditional ML project?
• Why might a technically excellent ML model still fail to deliver business impact?

Candidates must live and have the right to work in the UK for this role. We operate a hybrid working model with a 3:2 split (office working vs home working). 

 

Please note: We are not seeking new agency relationships at this time.