• Design, develop, and deliver solutions that will enhance and add value to new and existing data assets for broad industry application and aligned with business and market requirements. • Develop algorithmic and rule-based models and analytic capabilities based on rules engine, machine learning, mathematical modeling, pattern recognition, and data mining techniques. • Perform data analysis, feature engineering and model development using a variety of analytic techniques, tools, and programming languages to provide business insights and analytic products. • Create model reports for stakeholder review and model documentation. • Perform pattern recognition model creation and training using various types of algorithms and machine learning modeling techniques for unknown or less well-defined problems. • Develop analytics using Python, PySpark and other big data tools such as AutoML, BigQuery(BQ), BQML, DataPrep and Jupyter Notebook. • Develop analytics solutions using traditional database technology such as SQL and big data technology such as Hadoop, MongoDB, Cassandra, Redis, PostgreSQL and Neo4j, NoSQL databases. • Perform complex data analysis on large volumes of property and consumer data and present model performance to stakeholders.

• Scale up model inference and running predictions at scale on cutting-edge models • Work end to end to connect ML models to human interfaces (e.g. APIs, browsers, and applications) • Designing and implementing large-scale data and ML pipelines through a full end-to-end product development lifecycle • Collaborate with a cross-functional team of engineers, researchers, product managers, designers, and operations teammates to create cutting-edge products

• Build statistical models and production systems to balance advertiser performance with business goals. • Tune optimization parameters, measure internal competition, and model dynamic environments. • Design and run experiments to validate theories underpinning the mobile ad tech economy. • Develop applications in the areas of advertiser budget retention and growth, optimal margin allocation, and bidding innovations. • Collaborate with a team of world-class engineers with diverse backgrounds as well as peers across the broader company (e.g. Operations, GTM). • Use strong communication skills (verbal and written) to explain statistical and machine learning concepts to both technical and non-technical audiences. • Be part of an “engineering excellence” culture through state-of-the-art tools, risk-driven testing, explainable systems, and design/code review.

• Own the technical direction for pricing ML: Define what to build and how within the pricing engine, setting the strategy and roadmap for pricing machine learning as a core piece of tem's IP. • Build ML systems for price optimisation: Design and implement models that dynamically set prices, balancing the trade-off between signing probability, portfolio balance and margin maximisation. • Solve imbalance problems: Develop probabilistic models to optimise risk management and short-term balancing decisions in a highly dynamic environment. • Bridge modelling and production: Own the modelling and data layer while working closely with software engineers and MLOps to ensure models are architected for production, contributing to system design decisions that affect performance and reliability. • Communicate pricing decisions clearly: Articulate model behaviour, assumptions, and trade-offs to other technical stakeholders so that pricing decisions are understood across the teams that depend on them.