Lead Data Scientist at Betty Job Board

Sofia, Sofia-City, Bulgaria -

Full Time

Start Date

Immediate

Expiry Date

25 Jan, 26

Salary

0.0

Posted On

27 Oct, 25

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Science, Machine Learning, Predictive Modeling, Statistical Analysis, Python, SQL, Data Analysis, A/B Testing, Data Visualization, Cloud Technologies, Fraud Detection, Anomaly Detection, NLP, Time-Series Analysis, Causal Inference, ETL Processes

Industry

Entertainment Providers

Description

About us: Betty is an innovative entertainment company pioneering at the intersection of real money online casino and casual mobile gaming. Accredited by the Alcohol and Gaming Commission of Ontario (AGCO) as a B2C operator in February 2023, we've set a new standard in the industry. Our mission is to redefine the online casino experience by offering a uniquely transparent environment where players can relax, unwind, and enjoy themselves safely. We are committed to accessibility, fairness, and inclusivity, fostering a community of like-minded individuals who value ethical gaming practices and prioritize our players' safety and enjoyment above everything else. Our Values: We are honest – we value honesty in all aspects. Bring the Olives – we offer premium customer experience. Think Big – we believe in always striving for more. Key Responsibilities Design, build, test, and deploy predictive and prescriptive models (e.g., churn prediction, player lifetime value, segmentation, risk classification, fraud detection). Analyze large-scale player behavioral data to identify key value drivers and recommend optimizations for promotions, engagement, and monetization strategies. Develop models for anomaly detection, fraud prevention, and responsible gaming interventions to maintain a safe and compliant environment. Design and evaluate A/B and multivariate tests for product features and marketing campaigns, ensuring statistically robust insights. Partner with Data Engineers to ensure model pipelines are production-ready, automated, and scalable across cloud environments. Visualize and present analytical findings to both technical and non-technical stakeholders, driving clear, data-informed decisions. Monitor and refine model performance, automate recurring analyses, and ensure data quality and reproducibility. Requirements 3+ years of experience in a Data Scientist, Machine Learning Engineer, or advanced analytics role (ideally within iGaming, FinTech, or other consumer-facing digital products). Strong theoretical understanding of machine learning and statistical algorithms (classification, regression, clustering, optimization). Proven experience applying models to solve business problems (e.g., forecasting, segmentation, retention, player value optimization). Skilled in Python (Pandas, NumPy, Scikit-learn, PySpark) and SQL (advanced querying, ETL processes, performance optimization). Experienced in analyzing massive datasets (100M+ rows) across multiple sources. Strong communication skills — able to explain complex technical concepts to non-technical audiences. Nice to Have Experience with AWS, Docker, Kubernetes, or other cloud and containerization technologies. Background in NLP, time-series, or causal inference modeling. Proficiency with data visualization tools (Tableau, Looker, or Power BI). Experience integrating data and models into production pipelines and dashboards. Familiarity with iGaming KPIs such as LTV, ARPDAU, Churn, and Player Retention metrics. Understanding of A/B testing frameworks and experimental design. What We Offer Competitive salary Premium Health insurance Career and skills development opportunities Fun and collaborative team environment New modern office space

Responsibilities

The Lead Data Scientist will design, build, test, and deploy predictive models while analyzing large-scale player behavioral data to optimize promotions and engagement strategies. They will also partner with Data Engineers to ensure model pipelines are production-ready and present analytical findings to stakeholders.