Responsibilities:

Define, own, and evolve Xsolla's company-wide data strategy, aligning data initiatives with the goal of improving monetization outcomes for game developers and publishers.
Translate strategy into execution: build and lead the teams, roadmaps, and infrastructure needed to turn Xsolla's data into a durable competitive advantage.
Partner with product, engineering, marketing, and operations teams to identify where data and machine learning can directly increase developer revenue and player lifetime value.
Build, guide, and evaluate machine learning and AI models — designing controlled A/B and multivariate tests to compare model approaches and rigorously validate which produce measurable monetization improvements.
Establish a scalable experimentation culture, including test design standards, statistical rigor, and clear frameworks for deciding when a model or approach should be rolled out broadly.
Own data governance, quality, security, and privacy practices, ensuring compliance with relevant regulations (e.g., GDPR) and internal policy across all markets Xsolla serves.
Build and scale a high-performing data organization, including data science, analytics engineering, and ML engineering talent.
Hire and grow a strong team of data scientists and analysts, setting the technical bar and career development path for the function.
Break down data silos across the organization and champion a data-driven culture among executive peers and cross-functional teams.
Report on data strategy progress, model performance, and monetization impact to the CTO and executive leadership team.

Qualifications & Skills:

Proven experience as a CDO, VP of Data/Analytics, or in a comparable senior data leadership role, ideally within gaming, monetization, payments, or a related technology sector.
Strong hands-on background in machine learning and AI, with direct experience building, training, and evaluating models — not solely managing teams that do.
Demonstrated experience designing and running A/B tests or other experimentation frameworks to compare model or product variants and drive decisions from the results.
A track record of translating data strategy into measurable business outcomes, ideally tied to revenue or monetization growth.
Deep knowledge of data governance, data architecture, and modern data infrastructure (cloud data platforms, pipelines, MLOps).
Strong executive presence and communication skills, with the ability to influence peers, the board, and cross-functional stakeholders.
Bachelor's degree in a quantitative field (Computer Science, Statistics, Data Science, Engineering, or related); advanced degree preferred.
10+ years of progressive experience in data, analytics, or ML leadership roles, including experience building and scaling teams.
Experience working within a high-performing organization that measures success by results and impact rather than activity — and holds their team to that same standard.