Help & Supportcareers@gravityer.com
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Data Leader
VP of Data leading a multidisciplinary team across data science, analytics, and infrastructure at a real estate fintech lender. Over the past nine years, I’ve built and now lead the company’s AI and data platform, from modeling and analytics through to the underlying data infrastructure, spanning document processing pipelines, computer vision systems, and predictive models that turn proprietary data into decision systems driving business outcomes. I treat data as a product: governed rigorously, architected for scale, and measured by the decisions it improves. The systems my team builds power core workflows across the business, accelerating loan processing, reducing operational friction, improving borrower experience, and enabling better decisions. I’ve taken AI initiatives from early prototypes to production systems operating at scale in a regulated environment, with a focus on reliability, auditability, and real-world performance, not just model accuracy. What drives my work: - Building teams that ship production-grade AI systems - Establishing data governance as a prerequisite, not a cleanup step - Closing the gap between what works in a demo and what holds up in production
University of Strasbourg
Doctor of Philosophy (PhD), Computer Science
January 1, 2009 – January 1, 2012
Kiavi
VP, Data
March 1, 2017 – Present
San Francisco Bay Area
University of California, San Diego
Research Scientist in Machine Learning
June 1, 2012 – Present
Greater San Diego Area
Cultural Fit Analysis
The candidate's extensive experience in leading data organizations and driving AI strategy aligns well with a senior, impactful role. The background in both industry (Kiavi) and academia (UC San Diego, University of Strasbourg) demonstrates adaptability and a blend of practical application and theoretical depth. However, the target role of 'Data Analyst' seems significantly misaligned with the candidate's VP-level experience and PhD background, suggesting a potential overqualification or a mismatch in career trajectory. The breadth of skills is high, but the diversity of projects is not explicitly detailed beyond the scope of their VP role.
Soft Skills & Operational Fit
The candidate's experience as VP, Data, leading multidisciplinary teams and setting company-wide data and AI strategy, suggests strong leadership, strategic thinking, and operational management skills. The focus on turning data into production systems and automating workflows indicates a results-oriented and efficiency-driven approach. However, specific soft skill assessments (e.g., psychometric test) are not available.