Sr. Data Scientist, Ads Experimentation

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We are looking for a senior data scientist to be a part of the Ads Data Science team. You will work closely with product managers, engineers, and data scientists to up-level the experimentation capabilities of the ads organization. You have a strong understanding of experimentation, are not afraid to challenge the status quo, and thrive in a cross-functional environment.  

Reddit is continuing to grow our teams with the best talent. We're completely remote friendly and will continue to be after the pandemic.

Responsibilities:

• Elevate the experimentation practice for the Ads org by creating the right processes, templates and tools catered for ads use cases.
• Lead an experimentation council that can approve/guide experiment designs for various product launches.
• Explore new experiment designs/techniques such as small sample enhancements, Bayesian approaches, causal inference methodologies, dosage response studies, staggered experiments etc and apply them successfully for complex experiments on the ads side of the business.
• Liaise with the centralized experimentation team in building platform features to accelerate ads experimentation.
• Guide various ads product teams in understanding network effects and novelty effects when running experiments.
• Track and own the experiment calendar for the ads org. Run biweekly experiment review discussions.
• Create frameworks for making metric trade-off decisions, especially when they span multiple organizations (e.g. ads vs consumer metric tradeoffs).

What We Can Expect From You:

• 6+ years of experience (for Bachelor's/Master's); 4+ years of experience (for PhD) degree in quantitative/modeling roles.
• 3+ years of experience with experimentation, preferably for a consumer-facing service/app.
• Proficiency with statistical analysis and programming languages (Python, SQL).
• Understanding of experimentation and causal inference analyses.
• Strong analytical and communication skills.
• Experience with digital advertising is preferred, but not required.

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