Design, build, deploy, and maintain machine learning models for advertising use cases such as bidding, ranking, pacing, and campaign optimization.
Own the end-to-end ML lifecycle, including data preparation, model training, evaluation, deployment, and monitoring.
Partner closely with engineering teams to productionize models and ensure reliable, scalable deployments.
Design and analyze experiments (A/B tests) and clearly communicate results to stakeholders.
Perform root-cause analysis on model behavior and implement improvements to ensure model stability and performance.
Develop new features and enhance model training and inference pipelines.
Analyze large datasets to generate insights that inform advertising strategies and product decisions.
Collaborate with cross-regional teams across the US, UK, Europe, and India to drive knowledge sharing and innovation.
Contribute to MLOps efforts to improve model deployment, monitoring, and operational efficiency.

Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, or a related field.
3+ years of industry experience building and deploying machine learning systems in production.
Strong programming and data manipulation skills in Python and SQL with hands-on experience using libraries such as Pandas, Polars, NumPy, Scikit-Learn, XGBoost, and CatBoost.
Solid understanding of machine learning techniques such as linear models and tree-based models, along with model evaluation methodologies.
Strong analytical and problem-solving skills with the ability to work with large and complex datasets.
Experience with Git, AWS, Docker, and Airflow is a plus.
Familiarity with advertising, e-commerce, or social commerce concepts such as auction theory, budget pacing, and search ranking is an added advantage.
An active Kaggle or GitHub profile showcasing relevant projects is a plus.

Senior Data and Applied Scientist

Key skills