Senior Data Scientist

Role Overview

• 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

Tech Stack

• BigQuery
• Google Cloud Platform
• Pandas
• Python
• PyTorch
• Scikit-Learn
• SQL
• Tensorflow

Benefits

• Vibrant international team operating in hi-tech environment
• Excellent compensation package
• myPOS Academy for upskilling and training
• Unlimited access to courses on LinkedIn Learning
• Refer a friend bonus as we know that working with friends is fun
• Teambuilding, social activities and networks on a multi-national level