Our Data team sits at the core of that growth. We build the ML systems, data pipelines, and platform capabilities that drive intelligent decisions across the business — from fraud and risk to personalisation and LTV. If you want your models to matter, this is the place.
The Problem You'll Own
Pricing is one of the most direct levers on revenue in a marketplace. At Eneba, we've built internal pricing algorithms that power our Featured Offers — and we want someone to take full ownership of that system and push it further.
You'll own the algorithm end-to-end: understanding how users respond to price, modelling willingness to pay at the individual level, running experiments, and shipping improvements that show up directly in revenue. You'll work at the intersection of ML, economics, and product — and the impact of your work will be measurable from day one.
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.
Parexel
Nagarro
Ad Hoc LLC
Clarifai
Parexel
Eneba
Eneba
Eneba