Senior Product Analyst – Kazakhstan
We are looking for a Senior Applied Machine Learning Engineer to own and deliver production-grade antifraud solutions that directly impact business outcomes.
This is a hands-on, applied role, focused on designing, deploying, and operating machine learning and rule-based antifraud systems in real production environments. You will work on real-time and batch decision flows, take ownership of model rollout and monitoring, and continuously iterate solutions based on fraud patterns, false positives, and business impact.
This role does not focus on academic ML or theoretical optimization. Instead, it emphasizes practical ML engineering, antifraud decision-making, and end-to-end ownership of deployed solutions.
You will work closely with Product Analysts, engineering teams, and other Data Science practitioners within Service Analytics.
This is a hands-on, applied role, focused on designing, deploying, and operating machine learning and rule-based antifraud systems in real production environments. You will work on real-time and batch decision flows, take ownership of model rollout and monitoring, and continuously iterate solutions based on fraud patterns, false positives, and business impact.
This role does not focus on academic ML or theoretical optimization. Instead, it emphasizes practical ML engineering, antifraud decision-making, and end-to-end ownership of deployed solutions.
You will work closely with Product Analysts, engineering teams, and other Data Science practitioners within Service Analytics.
- Own antifraud solutions end-to-end, from problem framing and feature design to production deployment, monitoring, and iteration based on real-world performance.
- Design, build, and productionize machine learning and rule-based antifraud models, integrating them into existing real-time and batch decision flows.
- Take ownership of antifraud models after release, including rollout strategy, performance monitoring, degradation detection, and rollback when required.
- Continuously analyze fraud patterns, false positives, and false negatives to improve detection quality and minimize negative impact on legitimate users.
- Develop and maintain reliable feature pipelines for antifraud use cases, working with large-scale event and user-level data.
- Ensure antifraud solutions are production-ready, handling data quality issues, missing or delayed signals, evolving fraud behavior, and concept drift.
- Collaborate closely with Product Analysts, backend engineers, and Trust & Safety stakeholders to align ML decisions with business and operational needs.
- 4+ years of experience in applied machine learning, data science, or ML engineering, with a strong focus on fraud detection, abuse prevention, or behavioral analysis.
- Proven experience owning antifraud solutions end-to-end, from problem definition and modeling to production deployment and iteration beyond offline experiments.
- Strong Python engineering skills, with hands-on experience writing production-oriented code for data processing, feature engineering, and model inference.
- Practical experience with Python DS and ML libraries including pandas, NumPy, scikit-learn, and gradient boosting frameworks (e.g., LightGBM or XGBoost).
- Experience working with highly imbalanced datasets and understanding fraud-specific trade-offs between precision, recall, and business impact.
- Hands-on experience with model evaluation and interpretability, including tools such as SHAP or equivalent approaches.
- Experience analyzing large-scale event or user-level data in batch or near–real-time environments.
- Ability to independently structure complex, ambiguous antifraud problems and deliver practical, scalable, production-ready solutions.
- Strong collaboration and communication skills, with experience working effectively alongside Product Analysts, engineering teams, and other Data Science practitioners.
- Stable salary, official employment
- Health insurance
- Hybrid work mode and flexible schedule
- Access to professional counseling services, including psychological, financial, and legal support
- Discount club membership
- Diverse internal training programs
- Partially or fully paid additional training courses
- All necessary work equipment
