• Support the development of predictive models across the student lifecycle, including lead scoring, conversion propensity, retention risk, and demand forecasting. • Assist in building and evaluating generative AI solutions, such as LLM-powered agents, prompt engineering workflows, and retrieval-augmented generation (RAG) pipelines. • Perform exploratory data analysis, feature engineering, and data wrangling to prepare datasets for modelling. • Write clean, well-documented Python code for analysis and model development. • Help productionise models by supporting pipeline development, testing, and integration with downstream systems. • Prepare visualisations, summaries, and presentations that communicate analytical findings and model outputs to non-technical stakeholders. • Contribute to the team’s knowledge base by documenting methods, sharing learnings, and participating in code reviews. • Proactively develop your own skills through structured learning, certifications, and hands-on experimentation.

• Build, maintain, and improve dashboards and reports that give stakeholders clear, timely visibility into performance. • Write and optimise SQL and Python to extract, transform, and analyse data from multiple sources. • Partner with stakeholders to understand their questions, define metrics, and translate requirements into analytics that drive decisions. • Ensure data quality and accuracy through validation, de-duplication, and reconciliation of reporting outputs. • Document analyses and reporting logic, and present findings clearly to both technical and non-technical audiences.

Role Description At Zingbrain, we build real-time personalization systems for iGaming platforms. Our models operate in production, influencing what each user sees — from game recommendations to sportsbook event suggestions — based on live behavioral, transactional, and contextual data. We’re looking for a Senior Data Scientist to join our team and help us in the following areas: - Develop ML-driven features for casino games using supervised learning (regression, ranking, classification) - Maintain and enhance the existing recommendation systems in production, including: - Model enhancement using gradient boosting methods - Data cleaning and preprocessing - Pre- and post-processing workflows - Optimization of training and inference pipelines - Integration of ML models into Airflow pipelines in a multi-tenant environment - Adopt and configure the solution for different clients (tenants) This is a hands-on role involving modeling, experimentation, and close collaboration with engineering and product teams in a high-load, real-time environment. As a part of our team you will: - Collaborate with cross-functional teams of data scientists, engineers, product owners, designers, and researchers to ensure project success - Analyze large datasets to extract actionable insights that inform product decisions - Propose, implement, and evaluate machine learning approaches to solve business problems, working closely with Product Owner(s) - Maintain and adopt the current recommendation solution in a multi-tenant environment - Influence product strategy through research and experimentation that deepens understanding of how product features, platforms, and promotions affect user behavior Qualifications - 5+ years of experience in data science - A degree in a quantitative field (e.g., Mathematics, Statistics, Computer Science) Requirements - Proficiency in Python, SQL, and data manipulation tools (Pandas, Polars) - Strong engineering skills to design, build, and maintain scalable ML solutions, including implementing observability across pipelines through metrics, logging, and alerting - Knowledge of Docker, Kubernetes - Ability to structure and solve loosely defined problems, delivering actionable insights for product development; Strong analytical mindset with both numerical and business understanding - Hands-on experience with supervised ML techniques (regression and ranking using XGBoost, LightGBM, CatBoost, or neural networks), including feature engineering, model evaluation (AUC, NDCG, MSE, uplift metrics), and personalization or recommendation systems - Proven experience deploying ML models to production for near real-time or batch processing - Solid knowledge of statistical methods (A/B testing, significance testing, etc.) Benefits - Wellness program - Medical compensation - Paid sick leaves - Compensation for sports activities - Well-being webinars and workshops - Work & life balance - Wellness Day: 4th Friday off monthly - Remote work - 21 working days of vacation - 5 personal days per year - Professional development - English speaking club - Language learning bonus €150 per month - 80% paid professional employee training - Provided tech equipment - €150 for the arrangement of the workplace - Bonuses for significant events and additional personal days if necessary - Offline and online company parties and team buildings

• Lead and manage a team of senior associates and associates within the Banking/Investing vertical, driving team performance, productivity, and adherence to KPIs, identifying training needs and support capability building within the team • While performing research, oversee end-to-end project execution within the Banking/Investing domain • Ensure timely delivery of high-quality research • Develop and share subject knowledge expertise with team members, to help them navigate complex projects and act as a Subject Matter Expert (SME) for domain-specific queries • Support onboarding and training new team members, conduct knowledge-sharing sessions on Banking/Investing metrics • Develop and maintain SOPs, guidelines, and knowledge of repositories • Collaborate with analysts, to understand requirements and align on deliverables • Provide regular updates on progress, challenges, and key insights • Track and evaluate team performance metrics, provide regular feedback to members in areas to improve, ensure timely delivery of projects with high accuracy (target: ≥97%) • Proactively manage escalations and resolve delivery challenges • Drive continuous improvement initiatives within the team, identify opportunities to improve workflows, and optimize processes