The role requires experience in areas such as predictive modeling (classification and regression), optimization, industrial engineering, demand forecasting, and time-series forecasting. The ideal candidate will have working experience in deploying data science and machine learning models with large datasets in an industry setting. Deep understanding and ability to compare predictive models, evaluate their strengths and weaknesses in a particular context, and interpret and explain model results to a broad group of technical and non-technical stakeholders are also a plus. Driven candidates will also have experience working closely with data engineers to build efficient, scalable solutions. We are looking for a thoughtful and creative data scientist to join a team that creatively adapts state-of-the art analytical techniques to real-world business problems with an approach focused on rapid development, evaluation, and iterative improvement. Continuous learning is part of the job, both as it relates to technical skills and within the content and format of advertising domains (e.g. Apple News, Major League Soccer).
Bachelor's or equivalent experience in computer science, mathematics, or another quantitative field
Command over Python and SQL.
Comfort with cloud technologies such as AWS and Snowflake.
Experience with Big Data tools such as Hadoop, Spark and PySpark.
Experience in quantitative analysis including regression, classification, linear optimization, supply chain analytics, and time-series analyses.
Ability to communicate the results of analyses in a clear and effective manner with product and leadership teams to influence the overall strategy of the product.
Ability to partner with engineering, meet the data needs of the business, find creative analytical solutions, and develop initial prototypes to address business problems.
Experience with end-to-end implementation of a model prototype specifically training, processing, feature engineering, evaluating model outputs, and putting models into production.
Masters' degree, Ph.D. or equivalent experience in a quantitative field.
Experience in the digital advertising industry or a related field and/or experience with demand forecasting
Willingness to learn, both technically and in the domain of the data.
Familiarity with packages like numpy, pandas, scikit-learn, and prophet
Familiarity with job orchestration frameworks like git, Airflow, CI/CD, Kubernetes, Docker, Jenkins.
Experience and/or interest in deep learning, LLMs, and Natural Language Processing.