• You will bring statistical rigor to business decisions. • As part of the BI team, your mission is to go beyond descriptive analysis by applying statistical and machine learning models to find patterns, validate hypotheses, and generate forecasts that guide real strategies. • Model complex problems using Python. • Work with large volumes of data in ClickHouse and BigQuery. • Collaborate with product and operations teams to ensure models deliver real value. • Conduct statistical analyses to answer business questions: hypothesis testing, correlation analysis, segmentation, and predictive modeling. • Perform exploratory data analysis (EDA) to understand the data before modeling, identifying patterns and inconsistencies. • Write complex SQL queries in ClickHouse and BigQuery to extract and prepare data for analysis and modeling. • Use Python for data manipulation, exploratory analysis, and model prototyping. • Communicate results and insights clearly to business teams — translating statistics into decisions. • Collaborate with the BI team to define metrics, ensure data quality, and establish analytical best practices. • Monitor models in production, tracking performance and proposing continuous improvements.

• Design, implement, and optimize core bidding algorithms and auction mechanisms • Architect and scale bid landscape forecasting capabilities • Analyze large-scale marketplace dynamics to uncover deep insights and deliver algorithmic improvements • Drive offline evaluations and online A/B experiments to validate model performance and deliver measurable business impact • Collaborate cross-functionally with product, infrastructure, and engineering teams

• Design, implement, and optimize core bidding algorithms and auction mechanisms • Architect and scale bid landscape forecasting capabilities • Analyze large-scale marketplace dynamics to uncover deep insights and deliver algorithmic improvements • Drive offline evaluations and online A/B experiments to validate model performance and deliver measurable business impact • Collaborate cross-functionally with product, infrastructure, and engineering teams

• Own the production recommendation infrastructure: maintain and improve the systems that serve personalized content to millions of users, ensuring reliability, low latency, and scalability as the catalog and user base grow. • Research and prototype advanced recommendation algorithms: explore newer approaches - deep learning-based models, contextual bandits, session-based recommendations, graph-based methods - evaluate their potential, and run controlled experiments to validate uplift before production. • Produce ML models and pipelines: take prototypes (from yourself or from the team's Data Scientist) and turn them into production-grade, monitored, maintainable features integrated into the live recommendation engine. • Design scalable infrastructure: anticipate bottlenecks and design systems that can handle larger catalogs, more complex segmentations, and higher traffic - including serving layer optimization, caching strategies, and pipeline orchestration. • Build and maintain data pipelines in DBT and Databricks, ensuring clean transformations, data quality, and robust experimentation frameworks that the team can rely on. • Monitor model health in production: define retraining strategies, detect drift, and ensure recommendation quality is measured and maintained over time. • Collaborate closely with the Data Scientist and Senior Analyst to translate statistical insights and business requirements into engineering decisions.