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Tech Stack
Tools & technologiesBigQueryGoogle Cloud PlatformPandasPythonPyTorchScikit-LearnSparkSQLTensorflow
About the role
Key responsibilities & impact- 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
What you’ll need- 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).
Benefits
Comp & perks- Excellent compensation package
- 25 days annual paid leave (+1 day per year up to 30)
- Full “Luxury” package health insurance including dental care and optical glasses
- Meal vouchers of 102.26 EUR per month
- Fully covered Multisport card
- Fully covered public transport pass for Sofia
- Free coffee, snacks and drinks at the office
- Annual salary reviews, promotions and performance bonuses
- myPOS Academy for upskilling and training
- Unlimited access to courses on LinkedIn Learning
- Annual individual training and development budget
- Refer a friend bonus as we know that working with friends is fun
- Teambuilding, social activities and networks on a multi-national level
ATS Keywords
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Hard Skills & Tools
Pythonpandasscikit-learnXGBoostLightGBMPyTorchTensorFlowSQLMLOpscausal inference
Soft Skills
communicationpresentationstakeholder engagementteam leadershipanalytical thinkingproblem-solvingcollaborationtechnical documentationexperiment designstatistical analysis
Certifications
MSc in Computer SciencePhD in StatisticsPhD in Applied MathematicsPhD in Econometrics