Deployment Strategy Director (Hiring Immediately)

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.

We do this as an active co-sponsor, co-underwriting deals with top-tier Private Equity firms and deploying our own in-house team of data scientists, engineers, and strategists to execute value creation projects at the companies we invest in. Investing across North America and Europe, we currently have four 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 Directors are senior leaders who bring deep commercial expertise, a strong personal brand, and the experience to act as a trusted advisor to both PE partners and portfolio company leadership. You will hold oversight across multiple WovenLight projects simultaneously, shaping how we engage across our target sectors and building the relationships and reputation that support our continued growth.

Deployment Strategy Directors lead on:

• Providing oversight and strategic direction across multiple diagnostic and deployment projects, acting as the escalation point for complex delivery, risk, or stakeholder issues
• Owning senior relationships with GP partners and portfolio company C-suite, guiding strategic and operational decisions — especially on how to build lasting and sustainable AI and data capabilities
• Challenging and refining project direction to ensure alignment with each GP's value creation priorities and the specific commercial dynamics of the business
• Driving value attribution across projects, GPs, and portfolio — ensuring the link between WovenLight's work and enterprise value is clearly demonstrated
• Leading external eminence for WovenLight — speaking engagements, publications, and LP/GP showcases
• Actively leading business development, cultivating relationships with PE firms, and contributing to proposals and deal origination
• Coaching and developing Deployment Strategy talent and building future leaders of the function
• Coordinating investment, pipeline, and people strategy with Applied Engineering and Investment function leads

We invest across a range of sectors — with current focus on Services, Industrials, Healthcare, and Consumer & Sports. Candidates with deep domain expertise in one or more of these areas, and an existing network within relevant PE or industry ecosystems, will be particularly well positioned.

What We're Looking For

Business Leadership & Stakeholder Management

• Proven experience (7+ years) leading or overseeing commercial transformations, with a track record of driving adoption of new technologies and processes at scale
• Established relationships with C-suite and board-level stakeholders, with the ability to guide complex strategic and operational decisions
• Experience working alongside or advising PE firms, with a strong understanding of how investment theses are formed, tested, and realised
• Ability to build and deepen relationships with top-tier GPs by demonstrating execution excellence, operational empathy, and clarity of impact

Commercial & Financial Acumen

• Strong commercial understanding spanning multiple industries and value chain functions, with the ability to identify, articulate, and attribute value creation opportunities at a portfolio level
• Deep familiarity with PE deal dynamics — including deal structuring, EBITDA drivers, and exit positioning
• Track record of positioning AI and data science as a strategic value creation lever, not a cost centre
• Domain experience in one or more of our target sectors — Services, Industrials, Healthcare, or Consumer & Sports — and ideally an existing network within relevant PE or industry ecosystems

Technical (ML, AI, Software Development) Understanding

This is not a hands-on technical role, but Deployment Strategy Directors 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?