Data Science and AI builder

SciPlay is a leading developer and publisher of digital games on mobile and web platforms, providing highly entertaining free-to-play games that millions of people play every day for their authenticity, engagement and fun.
SciPlay currently offers nine core games, including social casino games Jackpot Party Casino, Gold Fish Casino Slots, Hot Shot Casino and Quick Hit Slots, and casual games MONOPOLY Slots, Bingo Showdown, 88 Fortunes Slots, Backgammon Live and Solitaire Pet Adventure with offices all over the world!
Position Summary
We are looking for a Data Science and AI builder with a strong background in applied machine learning, experience working with real business data, and a product-oriented mindset. In this role, you will transform game, player, and operational data into models and analytical products that support business decisions, from problem definition and feature engineering to model evaluation, deployment, and monitoring.
The role requires hands-on modeling skills and the ability to clearly explain assumptions, evaluation methods, uncertainty, and model limitations.
Key Responsibilities
Translate business questions into measurable modeling objectives, hypotheses, baselines, and success criteria.
Prepare and explore data, define labels, engineer features, and identify data-quality issues, leakage, bias, and missing data.
Build and compare models for regression, classification, clustering, forecasting, anomaly detection, ranking, and segmentation.
Design robust evaluations using holdout sets, cross-validation, backtesting, error analysis, and business-relevant metrics.
Collaborate with product owners, analysts, game teams, and operations to define the decisions each model will support.
Connect model performance to business outcomes and operational thresholds.
Apply LLM and agent capabilities when they improve workflows or products.
Deliver production-ready solutions with testing, documentation, versioning, and appropriate review processes.
Requirements
2–3 years of experience in applied data science, machine learning, or a related field, including ownership of at least one model from development through evaluation.
Experience with problem formulation, feature engineering, algorithm selection, tuning, validation, and error analysis.
Strong Python and SQL skills, with hands-on experience using pandas, NumPy, scikit-learn, or similar libraries.
Solid foundations in statistics and model evaluation, including sampling, cross-validation, leakage, class imbalance, uncertainty, and causal limitations.
Practical experience in at least one area such as forecasting, anomaly detection, classification, regression, segmentation, or ranking.
Hands-on experience with AI tools such as Claude, ChatGPT, Codex, or similar tools for building working products.
Experience productionizing models through scheduled workflows, services, or APIs, and monitoring data quality, model performance, drift, and failures.
Strong problem-solving and communication skills, with the ability to explain technical decisions, results, tradeoffs, and limitations to business partners.
Experience in mobile gaming, social casino, live operations, game analytics, or another data-intensive consumer product domain.