What you’ll do:

• Product recommendations. Clearly communicate recommendations to product and engineering leadership on how we can evolve our Notifications strategy to address shortcomings observed through deep analysis. 
• Develop measurement frameworks and metrics to drive data-driven decision making, optimizing for the full user journey.
• Opportunity sizing and analysis. Write clear, actionable analyses that help teams identify areas of improvement to our growth strategies. 
• Thought partner to product, engineering, and ML leadership on the Notifications team to prioritize/scope projects and inform the development of the notifications user experience and corresponding ML systems. 
• Improve machine learning models which power notifications delivery and content, via direct contributions, new features/signals, and/or compelling analyses. 
• Leadership: Lead and mentor the scope of work for at least 2 other data scientists, demonstrating high-quality output of both yourself and others for whom you are responsible. Provide continuous and candid feedback, recognizing individual strengths and contributions and flagging opportunities to improve performance.
  
  

What we’re looking for

• 8+ years of combined post-graduate academic and industry experience applying scientific methods to solve real-world problems on web-scale data
• Strong background in statistics, machine learning, and/or experimentation
• Expertise in at least one scripting language (ideally Python/R)
• Proficiency in SQL/Hive
• Strong business and product sense: delight in shaping vague questions into well-defined analyses and success metrics that drive business decisions.
• Excellent communication skills: able to lead initiatives across multiple product areas and communicate findings with leadership and product teams through compelling data narratives. 
• Experience working closely with cross-functional teams, iterating on a user product via A/B experimentation. 
• Experience leading key technical projects and substantially influencing the scope and output of others

What skills are ideal but not required?

Exposure to one or more of the following areas is beneficial. Ideal candidates would have exposure to 2+ of these areas and deep familiarity in 1 or more areas.

• Experience with ranking or recommender algorithms.
• Statistical rigor. Experience with causal inference projects.
• Experience working with ML teams and developing Overall Evaluation Criteria to drive decision making. 
• Experience working in a Growthfunction.
• Experience working with user research to complement learnings from data science methods. 

Relocation Statement:

• This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.

#LI-NM4

#LI-REMOTE