Role Description We’re looking for a Senior Product Data Analyst to join our team and help us discover valuable business insights hidden in large datasets to deliver an even better product to our VR users. You will analyze the data to track a variety of metrics to enhance product engagement and improve customer experience. - Collaborate with product, engineering, and marketing teams to define and monitor critical KPIs - Build and maintain dynamic dashboards and detailed reports to monitor and analyze performance across key product areas - Design, execute, and evaluate product experiments - Translate complex analytical insights into actionable strategies and recommendations for key stakeholders - Identify, diagnose, and resolve data quality issues to ensure accuracy and reliability of analytics processes - Develop predictive models to anticipate user behavior and proactively identify areas for product improvement Qualifications - 3+ years of experience in data analytics in a high-growth environment - Proficient in SQL and Python, with proven ability to manipulate large datasets, extract meaningful insights, and develop actionable conclusions - Demonstrated experience creating advanced dashboards preferably using Tableau - Proven background in designing, executing, and analyzing A/B testing - Strong ability to translate data into compelling narratives for both technical and non-technical audiences Requirements - Evaluate the potential benefits of a proposed product feature, including designing experiments to validate its impact on key metrics - Develop product hypotheses to boost retention - Analyze the adoption, engagement, and performance metrics of newly launched features, identifying actionable insights for improvement - Investigate sudden declines in key metrics, identifying root causes and proposing immediate corrective actions - Create user segmentation models to enhance email campaign targeting Benefits - This is a fully remote contract role. B2B cooperation only – you will work with us as an independent contractor operating through your own legal business entity (e.g., sole proprietorship, limited company, or equivalent). - The chance to be part of a pioneering team in a rapidly evolving industry - Direct impact on the future of media and technology - Flexible working hours to accommodate your lifestyle - Work remotely from almost anywhere - A culture that values innovation, accountability, and collaboration - Access to the tech you need from day one - Unlimited DeoVR Premium subscription - A dynamic and innovative work environment in a cutting-edge industry

• We are specialists in technological transformation, combining human expertise with AI to create scalable tech solutions. • With over 8,000 CI&Ters around the world, we have partnered with more than 1,000 clients during our 30 years of history. • Artificial Intelligence is our reality.

• Analyze Medicaid claims, visit, and billing datasets using SQL and other analytical tools. • Identify patterns and anomalies that may indicate fraud, waste, or abuse, including: Visit overlaps and impossible or implausible service combinations, Inflated, duplicate, or unbundled billing, Provider billing spikes or outlier utilization patterns, Inconsistencies in electronic visit verification (EVV) data, Suspicious provider enrollment or credentialing indicators, Patterns indicative of upcoding, place-of-service manipulation, or beneficiary identity issues. • Develop and refine detection queries and analytical logic that can be applied across datasets at scale. • Conduct proactive data analysis to identify emerging fraud patterns and program integrity risks. • Apply knowledge of the end-to-end revenue cycle — including claims submission, adjudication, remittance, and denial/appeal workflows — to contextualize billing anomalies and assess their integrity implications. • Apply machine learning and AI techniques to fraud detection, including anomaly detection models, predictive risk scoring, and unsupervised clustering of suspicious billing behavior. • Collaborate with data science teams on feature engineering, model validation, and the operationalization of AI-driven detection logic. • Leverage generative AI and LLM-based tools to support investigation summarization, pattern narrative development, and analytical workflow acceleration. • Stay current on emerging AI/ML applications in healthcare payment integrity and recommend adoption of relevant tools and techniques. • Test, validate, and continuously improve fraud detection models and analytical tools as they are developed and refined. • Translate analytical findings into clear, actionable requirements for product and engineering teams. • Contribute to the design of fraud detection dashboards, alerting systems, and investigation workflows. • Support the development of automated detection tools and AI-driven fraud identification capabilities. • Serve as a subject matter expert on FWA and program integrity concepts to ensure detection logic is clinically and operationally sound. • Present analytical findings and insights to internal stakeholders and payer clients — including state Medicaid agencies and managed care organizations — in a clear and actionable format. • Support client discussions related to fraud detection strategy, program integrity reporting, and regulatory compliance obligations. • Advise payer and state partners on detection methodologies aligned with CMS program integrity expectations, Medicaid Integrity Program (MIP) standards, and applicable federal regulations. • Document analytical methodologies and investigation approaches to support compliance, audit readiness, and regulatory expectations.

• Structuring Strategic Dashboards: Develop dashboards in Power BI or Looker Studio to meet specific data processing needs; • Data Warehouse Management (BigQuery): Organize, clean, and model tables within BigQuery to ensure data is ready for consumption; • Ingestion and Processing of External Data: Build pipelines to import, clean, and validate data from various files (CSV, spreadsheets), ensuring integrity when integrating into the central database; • Exploratory Analysis: Investigate datasets to find patterns and insights that support the GovTech strategy; • Support in Defining Processes: Actively contribute to building the team’s data culture by helping define naming conventions, folder organization, and update rituals; • Digital mindset, curiosity about new technologies, and interest in understanding how Artificial Intelligence can transform processes, experiences, and outcomes.