Senior Data Scientist Job at myPOS (Warsaw)

Job Description

As a Senior Data Scientist at myPOS, you will be the technical cornerstone of a lean, high-calibre team. Reporting directly to the Head of Data Science, you will independently own and deliver the most complex modelling programmes - setting the technical standard and ensuring that model work reaches production and creates measurable commercial value. This is a role for a data scientist who has outgrown purely executing on defined problems and is ready to shape how problems are framed, lead architectural decisions, and hold end-to-end accountability for outcomes - not just model performance.

Job Responsibility

Own the full lifecycle of complex modelling programmes across Customer & Commercial Intelligence: CLTV, Churn Prediction, Propensity to Buy, and Next Most Likely Product (NMLP)
Architect multi-horizon churn models and build the churn intervention scoring layer that prioritises at-risk merchants for Account Management teams
Lead the NMLP engine - designing and productionizing a multi-output recommendation system that identifies the next product across myPOS's full catalogue
Take technical ownership of core fraud model components: transaction-level classifiers, merchant behaviour anomaly detectors, and new-account fraud scorers optimised for high-throughput, low-latency inference
Architect and own the Next Best Action (NBA) decisioning engine - a real-time system that selects the highest-expected-value action for each merchant at every interaction
Design and build production-grade agentic AI systems that automate high-value analytical and operational workflows
Define and execute experiment designs for online evaluation - A/B tests, uplift experiments, and bandits - and analyse results with statistical rigour
Set and enforce technical standards across the team: code quality, reproducibility, evaluation rigour, model documentation, and MLOps practices
Produce high-quality model documentation and present complex modelling work clearly 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 commercial experience)
7+ years of applied data science and ML experience in a commercial environment, with a strong portfolio of models in production that drove measurable business outcomes
Expert Python for data science and ML engineering: pandas, scikit-learn, XGBoost / LightGBM, PyTorch or TensorFlow
clean, tested, modular code as a default
Deep expertise across the ML methodological spectrum: survival analysis, time-series and sequence modelling, uplift and causal inference, anomaly detection, and recommendation systems
Proven end-to-end ownership of at least three of: CLTV models, churn models, propensity models, fraud/risk models, recommendation or NBA systems - in a production commercial setting
Strong MLOps capability: feature stores, model registries, model serving infrastructure, drift monitoring, and CI/CD for ML pipelines
Deep SQL and data platform proficiency (GCP / BigQuery strongly preferred)
experience with streaming architectures for real-time feature generation
Hands-on expertise building LLM-powered applications: RAG pipelines, tool-use agents, multi-agent orchestration, and agent evaluation frameworks
Strong experience with causal inference methods: uplift modelling, difference-in-differences, or instrumental variables
Excellent communication: able to present complex technical work to senior business stakeholders and write high-quality model documentation

Nice to have

Experience in payments, fintech or financial services
Experience with reinforcement learning or contextual bandits for ranking and decisioning
Knowledge of graph neural networks for fraud or relationship modelling
Familiarity with AI governance frameworks (EU AI Act, SR 11-7)
Published research or open-source ML contributions
Experience with real-time streaming inference (Flink, Spark Streaming)