Data Science & Algorithms Team Lead – AdTech

• Lead, mentor, and grow a team of data scientists, ML engineers, and analysts
• Own the end-to-end lifecycle of algorithmic solutions — from translating business needs into a data science problem formulation, researching and proposing solutions, defining required data sources and transformations, and designing the supporting infrastructure, to establishing production-grade testing, monitoring, and value measurement
• Design and constantly deliver low-latency algorithms and their modifications, in production. Including designing low latency feature store
• Define and drive monitoring, QA, data drift, and model quality standards, ensuring reliability and continuous improvement
• Work closely with DevOps/MLOps to ensure the solution runs on scalable and highly reliable infrastructure
• Coordinate and align efforts across multiple teams — Data, DataOps, MLOps, Analytics, BI, and QA — to ensure smooth delivery and execution
• Collaborate closely with Product, Engineering, Infrastructure, and Business stakeholders in a fast-paced environment
• Drive the team’s technical direction, actively participating in idea generation, solution design, and design reviews

• MSc in Computer Science, Mathematics, Statistics, or a related field
• Strong background in Data Science, Reinforcement Learning, and classical ML for tabular data
• At least 3 years of proven team leadership experience in real-time, production systems
• Experience leading or mentoring technical teams
• Strong hands-on skills in Python and common ML libraries (e.g., regression/classification, XGBoost / LightGBM, recommendation systems, etc.)
• Solid understanding of databases and SQL for data retrieval and analysis
• Familiarity with monitoring/alerting tools such as Grafana and Kibana
• Strong analytical mindset and ability to thrive in a dynamic and changing environment
• Excellent communication skills, with the ability to clearly explain complex technical concepts to management and cross-functional teams

Experience in the programmatic advertising ecosystem (DSPs, SSPs, MMPs, RTB exchanges) is a strong advantage