About the Job
You’ll be joining a multidisciplinary team of scientists, researchers, and engineers who research and innovate on the latest technology to create data and AI products that millions of people will experience every day.
This DS Mid role is hyper-focused on:
Customer segmentation: Building, evaluating, and deploying models to identify and target customer segments for personalized experiences and marketing.
Online controlled experiments: Designing, running, and analyzing A/B tests and other online experiments to measure the impact of new features, campaigns, and product changes.
Key Responsibilities
- Research, prototype, and develop AI solutions for personalized systems and customer segmentation.
- Design and analyze online controlled experiments (A/B tests) to validate hypotheses and measure business impact.
- Build, deploy, and analyze AI solutions; perform statistical experiments when deploying new AI products.
- Stay up to date with emerging technology and learn new technologies/libraries/frameworks.
- Collaborate with peers across data, product, and systems design disciplines.
- Deliver on time with a high bar for research, innovation, and engineering quality.
- Translate business goals into data science problems and communicate results to non-technical audiences. Proven track record of driving measurable impact through data science in marketing contexts.
Basic Qualifications
- 2+ years of experience with statistical data science techniques, feature engineering, and customer segmentation.
- 2+ years of experience with SQL, PySpark, and Python.
- 2+ years of experience training, evaluating, and deploying machine learning models.
- 2+ years of experience productionizing and deploying ML workloads in AWS/Azure.
- Familiarity with containerized application tooling and deployments (Docker/Kubernetes).
- Bachelor’s Degree or higher in Computer Science/Engineering/Math, or relevant experience.
Extra Credit
- Experience with Databricks platform.
- Experience building ML models with large amounts of structured and unstructured data.
- Experience working with MarTech platforms (e.g., CDPs, DMPs, ESPs) and integrating data science into marketing workflows.