VP Data Technology Transformation - Private Equity
Client Server · London, England ·
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Overview
As VP Data Technology Transformation, you will lead data strategy, diligence, and execution across the fund and portfolio companies. You’ll partner with c-suite investors and data leaders to align strategies, scale systems, and uncover monetisation opportunities. You’ll drive data diligence, create value creation plans, and assess data maturity to deliver operational improvements and innovation. This role blends strategic influence with hands-on governance and security to enable data-driven transformation.
Pay / Benefits
- Competitive salary up to 130k
- Bonus
- Hybrid working (2 days WFH per week)
- Extensive benefits package
Responsibilities
- Lead data strategy, diligence, and execution across the fund and portfolio companies
- Coordinate with c-suite investors and data counterparts to align data initiatives with business goals
- Drive data diligence efforts and develop value creation plans
- Conduct data maturity assessments and monitor the progress of data initiatives
- Implement data strategies to improve operations, enable scaling, and foster innovation
- Oversee data governance and information security to ensure GDPR/CCPA compliance
- Translate complex data concepts to stakeholders and push back when needed
Key requirements
- VP/Head of Data experience in financial services or consulting with focus on data diligence, value creation, and execution
- Strong technical background: Python, cloud environments (AWS, Azure, GCP) and ML libraries (PyTorch, TensorFlow, Scikit)
- Advanced experience in data maturity assessments and strategy development for business transformation
- Deep knowledge of data governance (GDPR, CCPA) and data security
- Excellent communication and stakeholder management, ability to engage c-suite
- STEM degree
- communication excellence
- stakeholder management
- ability to translate technical concepts for executives
- Python
- Cloud: AWS, Azure, GCP
- Machine learning libraries: PyTorch, TensorFlow, Scikit-learn
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