Our marketplace supports close to 20m+ active users (and growing fast!), provides a level of trust, safety and market accessibility unparalleled to none. We’re proud of what we’ve accomplished in such a short time and look forward to sharing this journey with you. Join us as we continue to scale, diversify our portfolio, and grow with the evolving community of gamers.

• Own and continuously improve  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

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• 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.

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• 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