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

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.