Responsibilities
- Work closely with product and business teams to translate business problems into data science solutions or data analytics researches
- Work closely with product and business teams to define clear success metrics and KPIs
- Work closely with product and business teams to turn insights into actionable recommendations that impact revenue and player behavior
- Ensure all models and analyses are aligned with real business outcomes, not just technical performance
- Design, build, and deploy ML models (e.g., churn prediction, LTV forecasting, revenue uplift, player segmentation) from experimentation to production.
- Define and implement standardized, scalable DS/DA processes across the team: Model development lifecycle (design → validation → production)
- Define and implement standardized, scalable DS/DA processes across the team: Code quality, documentation, and reproducibility
- Define and implement standardized, scalable DS/DA processes across the team: Experimentation and evaluation frameworks
- Continuously improve delivery efficiency, reducing time from idea → production
- Identify opportunities to automate manual analytical workflows using AI/ML
Requirements
- Bachelor’s or Master’s degree in Statistics, Mathematics, Computer Science, Economics, or a related quantitative field.
- 5+ years of experience as a Senior Data Scientist role/Lead role, leading both analytical and data science domain work.
- Strong SQL and Python skills; experience with large datasets, notebooks, reproducible analysis, and Git-based workflows.
- Solid knowledge of ML, statistics, experimental design, A/B testing, holdout evaluation, and business KPI analysis.
- Commercial experience with predictive modeling, classification, segmentation, forecasting, model evaluation, and business impact measurement.
- Strong analytical and problem-solving mindset; ability to find the story in the data: uncovering trends and explaining them in clear business terms.
- Commercial Experience working with large datasets and distributed computing tools (BigQuery, Spark, Hive, or similar).
- Solid understanding of experimental design and statistical testing (A/B testing, hypothesis testing, confidence intervals).
- Familiarity with causal inference techniques (e.g., Propensity Score Matching, Instrumental Variables, Difference-in-Differences, uplift modeling), and predictive modeling techniques.
- English B2+
Nice to Have
- Experience in gaming, gambling, fintech, e-commerce, subscription, or other user-behavior-driven products.
- Experience with BI tools such as Tableau, Looker, or Power BI.
- Product mindset: the ability to go beyond numbers and propose actionable solutions that make an impact.
Benefits
- Work on meaningful data products and shape them with your vision.
- 25 vacation days + 15 sick days + 1 birthday leave.
- Budget for English classes.
- Budget for health insurance.
- Annual education & development budget.
- Remote-friendly culture with a small, dedicated team.
Additional Information
- English B2+
