Senior Data Scientist - Advertising division

Description

The Advertising division of the company Hospitality drives demand and grows client market share by advertising hotels, cities, regions and airlines across web search, web, social, GDS and mobile channels.

The company runs a data-driven travel advertising platform built with partnerships with large advertising companies, reaching billions of travelers worldwide every day.

Machine learning systems make the platform's decisions on campaign delivery, budget allocation, audience targeting and real-time bidding. The company built its own proprietary ad technology stack rather than relying only on third-party platforms.

The platform uses a microservices architecture with Golang, Scala, Python, GraphQL, Apache Airflow, Redis and PostgreSQL. It handles 300k transactions per second, runs on a petabyte-scale BigQuery warehouse, makes up to 500k predictions per second and is hosted on Google Cloud Platform across 4 regions. Models are built with Python, TensorFlow, Keras, LightGBM and other frameworks.

As a Senior Data Scientist you will join the Bidder & Platform Optimization team, which owns machine learning solutions from problem definition and experimentation through production deployment and monitoring. Your models will shape advertising decisions and budget spend across global travel campaigns every day.

You will work with Data Engineers, Software Engineers, Product Managers and other stakeholders to turn business problems into scalable solutions across real-time bidding, campaign management and the data platform.

You will take part from early ideation and research, help set product direction and propose technical solutions. You will also have real influence over the technical direction of the optimization and machine learning roadmap.

The role combines large-scale data, real-time decisions and fast experimentation, so you will see how your work affects product performance and customer outcomes.

Responsibilities

• Build and keep improving machine learning and statistical models for audience targeting, real-time bidding, campaign optimization and budget allocation.
• Research and develop optimization strategies that mix AI and classical machine learning to improve campaign performance, bidding efficiency and business results.
• Forecast supply and demand to support better advertising decisions and budget use.
• Design experiments and success metrics, and use A/B testing and data analysis to evaluate and improve model performance.
• Put models into production and set up monitoring, feedback loops and governance to keep them reliable over time.
• Work with Data Engineers and Software Engineers to build and improve data pipelines, streaming systems and ML infrastructure.
• Work with Product Managers and business stakeholders to find opportunities, shape solutions and turn business problems into data science projects.
• Lead technical discussions, influence product and data science roadmaps and spread best practices across the organization.
• Guide and support other data scientists through technical advice, knowledge sharing and joint problem solving.

Requirements

• Solid grounding in statistics, experimentation, machine learning and data wrangling, including model validation and performance evaluation.
• Strong problem solving, with the ability to turn business and product challenges into data-driven solutions.
• Solid software engineering basics and production experience with Python and SQL.
• Experience deploying, running and monitoring machine learning systems in production.
• A record of delivering complex projects from concept to production, taking clear ownership.
• Clear communication, able to explain complex technical ideas to both technical and non-technical audiences.
• Works well across functions.
• Curious and pragmatic, with a real interest in using data and experiments to make decisions.
• Committed to quality, reliability and correctness.
• Hands-on experience with Google Cloud Platform (GCP) and BigQuery.
• Experience with machine learning in programmatic advertising, ad tech or related optimization areas.
• Experience building or running real-time machine learning systems.
• Familiarity with large-scale experimentation and online optimization techniques.