Machine Learning Engineer

📌Vacancy: ML Engineer
📌Format: Remote

An international iGaming product company is looking for an ML Engineer who will create analytics for user segmentation, recommendations and risk identification based on big data.
The company already has a powerful database and resources - all that remains is to develop models and implement them in work processes 🚀

📌Expectations from the candidate:

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.

📌Key responsibilities:

● 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.

📌 The company offers:

● Remote work options;

● Managers interested in developing team members;

● 20 working days of vacation, 5 days off and sick pay;

● Work with large volumes of real behavioral data to solve complex risk-related problems.

● Help define how machine learning replaces manual operations in highly sensitive areas.