Staff Product Data Scientist

The Digital Wallet Analytics & ML team at PayPal plays a critical role in driving acceleration for PayPal’s App Surface by using Advanced ML techniques, critical problem solving, the to advocate for decisions founded in data-driven insights. This team sits at the intersection of Strategy, Product, and Data Science, and has a front-row seat to Paypal's Growth strategy as we expand and engage our customer base of 400 million+ global users.

Your Way to Impact:

Seeking a Data Scientist for Pricing Optimization Analytics team who is enthusiastic about problem-solving, adept at generating valuable insights from data through deep dive analysis, experienced at evaluating performance of pricing strategies and initiatives by establishing test and control plans and ensuring constant monitoring is in place.

Your Day-to-Day:

• Become a domain expert in Digital Wallets and conduct rigorous data analysis to drive meaningful improvements to PayPal's customer experience
• Perform deep-dive analytics including Causal Inference analysis, Pre-Post analysis, Sensitivity analysis, financial projections, and additional ad-hoc exercises to provide holistic recommendations for various product launches
• Challenge the status quo, and drive data backed decision making 
• Partner closely with product leaders to understand new product offerings being built and recommend the right metrics to measure the performance of those features 
• Identify key metrics, conduct rigorous explorative data analysis, create executive-friendly info-insight packets and build business cases that drive decision making and prioritization 
• Analyze business performance and health, triage issues, and provide recommendation on the best course solution and optimization 
• Synthesizing large volumes of data with attention to granular details and present findings and recommendations to senior-level stakeholders 
• Collaborate with engineering and data engineering to enable feature tracking, resolve complex data and tracking issues, and build necessary data pipelines 
• Define and cultivate best practices in analytics, ML and experimentation 
• Support multiple projects at the same time in a fast-paced, results-oriented environment 
• Mentor junior analysts on complex analyses 
• You will collaborate on a wide array of product and business problems with a diverse set of cross-functional partners across Product, Engineering, Research, Data Engineering, Marketing, Sales, Finance and others
• Stay abreast of emerging Data Science/ AI/ ML technologies and methodologies, conducting research and experimentation to drive innovation within the team. 
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What You Need to Bring:

• A successful candidate will have at least 6+ years of experience analyzing large, multi-dimensional data sets and synthesizing insights into actionable solution
• Bring advanced technical expertise in experimentation, causal inferencing, Machine Learning or Product Analytics
• Communication and influence: You won't simply present data, but tell data-driven stories
• Fluent in SQL and scripting languages such as Python or R, comfortable working with large, complex, and potentially messy datasets 
• Understanding of statistics (e.g. hypothesis testing, statistical inference, regression) and experience designing and evaluating complex experiments 
• Exceptional communication skills, both written and verbal, to influence cross-functional teams 
• Strong interpersonal skills and experience leading cross-functional teams  
• Prior work experience in a product analytics space would be highly valued 
• A passion for problem-solving and comfort with ambiguity 
• Bachelor’s/Master’s degree in a quantitative field (such as Analytics, Statistics, Mathematics, Economics or Engineering) or equivalent field experience is preferred (Advanced degree is a plus but not needed)
• Experience managing or mentoring junior team members is a plus but not needed