ML Engineer (Gambling)

Our client is building a next-gen ML-driven risk management and automation system to reduce manual operations, detect fraudulent behavior, and improve user-level decision flows.
They’re looking for a Machine Learning Engineer who combines deep technical ability with strong data intuition — someone who’s comfortable working across the full pipeline: from raw logs and noisy events to deployed, monitored models that drive real-time or batch decisions.
You’ll be central to building internal intelligence for automated user segmentation, payout prioritization, payment method recommendations, and bonus risk detection — all backed by high-volume behavioral and transactional data.

What You’ll Be Doing:
● Data Exploration & Feature Engineering:
Dive into complex datasets - user activity, financial transactions, KYC logs - and extract meaningful patterns and risk signals.

Design robust and reusable feature sets for training, monitoring, and model
explainability.

● ML Model Development:
Build and evaluate models for fraud detection, user risk scoring, payment method ranking, and verification flow prediction.

Balance high class imbalance, cold-start edge cases, and evolving behavioral trends.

● Segmentation & Profiling:
Develop intelligent user clustering and segmentation frameworks based on
multi-dimensional activity and risk signals.

Enable downstream teams to personalize verification, UX, and promo logic.

● ML Pipeline & Deployment:
Create end-to-end pipelines: data preprocessing → feature stores → training → validation → batch/real-time inference.

Ensure model versioning, reproducibility, monitoring, and drift detection.

● Process Automation & Risk Tools:
Support automation of high-volume workflows (e.g., withdrawal reviews, user verifications) by integrating risk scores and confidence signals.

Help build internal tooling for analysts and risk teams to interact with model outputs and surface insights.

What You Bring:
Core Technical Skills
● Proficiency in Python for data science and ML (e.g., pandas, NumPy, scikit-learn, XGBoost, LightGBM, PyTorch).
● Strong SQL skills for deep-dive investigations and pipeline integrations.
● Experience with building production-grade ML pipelines using tools like Airflow, MLflow, Prefect, or similar.
● Skilled in feature engineering from raw logs or event streams (both user-level and transactional).
● Solid understanding of model evaluation (especially in high-imbalance, risk-heavy domains).
● Experience with data versioning and managing ML lifecycle in production (e.g., DVC, Feast, or custom solutions).

Data & Modeling Mindset

● Strong EDA (exploratory data analysis) skills and the ability to reason about data patterns, anomalies, and edge cases.
● Experience in binary classification, ranking, segmentation, and/or semi-supervised detection.
● Ability to design models and experiments that reflect real-world operational constraints and decision risks.

Bonus Points
● Background in fraud detection, risk scoring, or KYC/AML.
● Familiarity with real-time inference pipelines and streaming data processing (Kafka, Flink, Spark Streaming).
● Experience working in iGaming, payments, or financial services.
● Exposure to admin or ops tooling — model-driven UIs, analyst dashboards, or labeling workflows.

Why You Should Join:
● Work with high-volume, real-world behavioral data to solve complex risk challenges.
● Help define how machine learning replaces manual ops in high-sensitivity domains.
● Join a product-driven, execution-oriented environment where models ship to production and drive action.
● Collaborate closely with domain experts and see the real-world impact of your work daily.

Let’s make risk automation smarter.
Apply now and help us turn messy data into meaningful decisions.