PayPal Principal Economist 

United States, California, San Jose 

445917474

Your day to day:

• Investigating the current state of the art of experimentation practices and causal inferencing/ML techniques across multiple teams in the GADS Consumer Marketing org and identifying opportunities for upscaling the methodology best practices.
• Identifying improvement opportunities, developing test experimentation prototypes, and benchmarking results to challenge status quo and share the results with the stakeholders.
• Writing scalable code to automate testing frameworks, optimizing for reusability, and getting experimenters onboard with the tools, and capabilities created.
• Collaborating with cross-functional teams, including engineering, product, and marketing, to design, develop, review and track key performance indicators (KPIs) for experimentation.
• Conducting best practices literature review and sharing sessions with the entire org, and providing design review, guidance and sign-off support to stakeholders across the org.
• Conducting experiments to measure these KPIs, as well as deriving actionable insights from the data, to continually improve the technology and drive business outcomes.

What are we looking for

• PhD in quantitative science or engineering field (for example: Statistics, Biostatistics, Mathematics, Operations Research, Computer Science, Physics) with a minimum of 5 years of hands-on experience as an individual contributor in the field of experimentation.
• Strong theoretical foundations in probability & statistics, causal inferencing techniques (such as, RCTs, quasi-experimental designs,matching/propensity-based),general testing state of the art (such as, A/B, A/A, multivariate, sequential, split, redirect, bandit-based, personalization, factorial), machine learning.
• Proven expertise in setting up hypotheses to assess consumer behavior, in designing, implementing and deploying tests, in conducting post-hoc analysis, in assimilating best practices for a wide array of design use-cases and communicating the findings in a succinct manner to marketing stakeholders and different experimentation groups.
• Proficiency in building scalable packages/libraries for experimentation, in-depth statistical analysis, causal inferencing and visualization in R and Python.
• Fluency in processing and manipulating large datasets and building data engineering pipelines using technologies such Hive, SQL, BigQuery, or Spark.
• Proficiency in machine learning frameworks and packages, such as TensorFlow and PyTorch.

Nice to Haves

• Experience leading or participating in groups responsible for building out best practices for org-wide experimentation capabilities (such as, experimentation center of excellence).
• Experience participating in ideation and improvement of internal experimentation engineering platforms and working with external A/B testing platforms such as Optimizely, Google Optimize, VWO, Adobe Target or Convert.
• Prior experience working in a cloud-based environment such as GCP.

Travel Percent:

The total compensation for this practice may include an annual performance bonus (or other incentive compensation, as applicable), equity, and medical, dental, vision, and other benefits. For more information, visit .

The U.S. national annual pay range for this role is

$107300 to $259600

Any general requests for consideration of your skills, please