Data Scientist

• Own high-impact data science work across product data science and fair play
• Create and build novel algorithms, models, and data-powered features that directly shape the chess experience for our users
• Partner across the company on product vision and execution
• Work closely with product managers, engineers, designers, and writers
• Design and deploy creative heuristics, algorithms, and models that identify cheating at scale
• Raise the quality and sophistication of our approaches over time
• Manage a lean team of data scientists
• Turn strategy into clear team objectives and results
• Help data scientists grow into scientists who can operate with more speed and independence over time
• Communicate early and often

Senior Manager, Data Science

• Design and deploy lightweight agents for editorial intelligence and revenue/ad operations
• Pull and analyze data from sources like Google Ad Manager, Google Search Console, GA4, and third-party APIs
• Surface anomalies, trends, and performance signals automatically
• Deliver alerts, reports, and recommendations into Slack or Google Sheets
• Use LLMs (Gemini or equivalent) to generate content briefs, scoring outputs, and editorial angles

Data Scientist, Applied AI

• Develop, implement and optimize advanced algorithms, models and capabilities that help teams automate their workloads.
• Work on a variety of projects that involve understanding, processing and generating human language to solve complex problems and create innovative solutions.
• Design, develop and implement state-of-the-art algorithms and models, within the context of language models.
• Realize new AI-based capabilities in areas such as decision support, mission planning, workflow automation.
• Train and optimize large language models using vast amounts of textual data, ensuring high performance and accuracy.
• Perform data preprocessing tasks such as tokenization, stemming, lemmatization and normalization to prepare datasets for training and evaluation.
• Stay current with the latest advancements in LLM and Natural Language Processing (NLP) and apply new techniques to improve existing models and develop new solutions.
• Work closely with data engineers, software developers, product managers and other stakeholders to understand project requirements and deliver effective solutions.
• Evaluate the performance of models using appropriate metrics and techniques and iteratively improve their accuracy and efficiency.
• Collaborate with engineering teams to deploy models into production environments and ensure their robustness and scalability.
• Maintain comprehensive documentation of models, algorithms and processes for future reference and reproducibility.

Data Scientist – LLM Engineer

• Create Analytics Framework for Mobile Apps: Design and implement a robust analytics framework tailored to mobile and web applications, covering complete business cycle with marketing, monetisation, payments and product data. Esteblish key business metrics and reporting structure for financial and operational controls.
• Data Platform Management and Excellence: Take charge of the Data Platform’s ownership, ensuring it is scalable, secure, and efficient to support real-time data processing and analytics needs for business units. Oversee the integration of required data sources and the maintenance of data integrity across the platform. Define and manage Data Model inline with business needs.
• Operational Reporting and Visualization Tooling: Develop and maintain operational reporting systems and dashboards to monitor day-to-day operations, including key metrics for app usage, marketing and financial performance. Select and implement cutting-edge tools for data visualization to ensure accessible, real-time insights for decision-makers.
• Strategic Insights and Decision Support: Provide strategic insights to executive leadership and internal stakeholders by analyzing data trends and market dynamics. Drive data-driven decision-making processes across the company, particularly in areas affecting product development, marketing strategies, and customer experience. Design business metrics, create plans and forecasts.
• Cross-functional Collaboration for Data-Driven Culture: Foster a collaborative environment with Marketing, Monetization, Product, Payments, Finance and Customer Support teams to identify and fulfill their analytics needs. Champion the use of data and insights in all strategic discussions and planning sessions to embed a data-driven culture throughout the organization.
• Data Governance, Privacy, and Compliance: Ensure the analytics practices adhere to data governance standards and comply with relevant privacy laws and regulations. Implement policies and procedures for data handling and sharing to protect user data and company information.
• Leadership and Team Development: Assemble and steer the analytics team, setting objectives and key performance indicators in line with the company’s vision.

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