Senior Data Scientist – Contractor

This is a fully remote position, open to applicants in Poland.

📋 Description

• Take charge of the comprehensive lifecycle of intricate modelling programs within Customer & Commercial Intelligence, including CLTV, Churn Prediction, Propensity to Buy, and Next Most Likely Product (NMLP).

• Design multi-horizon churn models and establish the churn intervention scoring layer to prioritize at-risk merchants for the Account Management teams.

• Oversee the NMLP engine, crafting and operationalizing a multi-output recommendation system that identifies the next product from myPOS's entire catalog.

• Assume technical leadership of essential fraud model components, such as transaction-level classifiers, merchant behavior anomaly detectors, and new-account fraud scoring systems optimized for high-throughput and low-latency inference.

• Architect and manage the Next Best Action (NBA) decisioning engine, a real-time system that determines the highest expected value action for each merchant at every interaction.

• Design and develop production-grade agentic AI systems that automate valuable analytical and operational workflows.

• Define and implement experimental designs for online evaluations, including A/B tests, uplift experiments, and bandit algorithms, and rigorously analyze the results using statistical methods.

• Establish and uphold technical standards throughout the team, encompassing code quality, reproducibility, evaluation rigor, model documentation, and MLOps practices.

• Create high-quality model documentation and effectively communicate complex modelling work to stakeholders across Sales, Marketing, Risk, Product, and Operations.

⛳️ Requirements

• MSc or PhD in Computer Science, Statistics, Applied Mathematics, Econometrics, or a related quantitative field (or equivalent professional experience).

• Over 7 years of practical data science and machine learning experience in a commercial setting, showcasing a robust portfolio of production models that have driven measurable business outcomes.

• Proficiency in Python for data science and ML engineering, including tools such as pandas, scikit-learn, XGBoost / LightGBM, PyTorch or TensorFlow; with a focus on clean, tested, and modular code as standard.

• Extensive knowledge across the full spectrum of ML methodologies: survival analysis, time-series and sequence modelling, uplift and causal inference, anomaly detection, and recommendation systems.

• Demonstrated end-to-end ownership of at least three of the following: CLTV models, churn models, propensity models, fraud/risk models, or recommendation/NBA systems in a commercial production environment.

• Strong MLOps expertise, including feature stores, model registries, model serving infrastructure, drift monitoring, and CI/CD for ML pipelines.

• Proficient in SQL and data platforms (GCP / BigQuery preferred), with experience in streaming architectures for real-time feature generation.

• Practical experience in building LLM-powered applications, including RAG pipelines, tool-use agents, multi-agent orchestration, and agent evaluation frameworks.

• Solid background in causal inference methods, such as uplift modelling, difference-in-differences, or instrumental variables.

• Exceptional communication skills, capable of presenting complex technical work to senior business stakeholders and producing high-quality model documentation.

🏝️ Benefits

• Competitive compensation package.

• Access to myPOS Academy for professional development and training.

• Unlimited access to courses on LinkedIn Learning.

• Referral bonus program, as we believe working with friends is enjoyable.

• Opportunities for team-building, social activities, and networking on an international scale.