Machine Learning Engineer, Pricing Optimisation

Eneba is building a sustainable marketplace for gamers, supporting over 20 million active users. They are seeking a Machine Learning Engineer to own and improve their pricing algorithms, leveraging data to drive revenue through intelligent decision-making and experimentation.

Responsibilities:

• Own and continuously improve Eneba's Featured Offers pricing algorithm — from model design through experimentation to production monitoring
• Build and iterate on willingness-to-pay and price elasticity models using behavioural signals: purchase history, browsing patterns, session data, price sensitivity indicators
• Collaborate with Product and Marketing/Growth to define pricing strategies for promotional campaigns and featured placements
• Define and track evaluation metrics connecting model output to business KPIs — revenue per session, conversion rate, margin, promotional ROI
• Work with Data Platform and Backend Engineering to ship pricing models as low-latency APIs integrated into live marketplace surfaces
• Monitor deployed models for data drift, distribution shifts, and degradation; own observability and alerting
• Contribute pricing-relevant features to the feature store — user price sensitivity signals, historical purchase behaviour, category-level demand indicators

Requirements:

• Hands-on production experience building models that optimise pricing decisions — promotional pricing, demand-based pricing, or personalised pricing. You've shipped something that moved a revenue number
• Experience modelling willingness to pay, price elasticity, or conversion probability as a function of price. You're comfortable working with implicit signals and sparse, noisy data
• End-to-end ML ownership — you've taken models from raw data through feature engineering, training, evaluation, API deployment, and production monitoring. You don't hand off at the notebook stage
• Strong Python and MLOps fluency — extensive Python for model development, plus experience with MLOps tooling (MLflow or similar) for experiment tracking, model versioning, and lifecycle management
• Experience with bandit algorithms or reinforcement learning for online pricing optimisation
• Familiarity with causal inference methods (uplift modelling, difference-in-differences) for pricing experiments
• Real-time or streaming inference experience (Kafka, Flink) for session-aware pricing
• Familiarity with Databricks and/or Apache Spark for large-scale data processing
• Production experience with feature stores (Databricks Feature Store, Hopsworks, Feast, or similar)
• Background in marketplace economics, auction theory, or game-theoretic pricing
• Experience with setting up and evaluating A/B tests
• Strong business communication skills — you can translate model results and experimental findings into clear, actionable language for product and commercial stakeholders
• Good English level is required, proficiency is preferred