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Product Data Scientist, Consumer Payments

Google
Posted 11 hours ago, valid for 13 days
Location

Mountain View, CA, US

Salary

$138,000 - $197,000 per year

Contract type

Full Time

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Sonic Summary

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  • The job is for a Data Scientist with a minimum requirement of a Bachelor's degree in a quantitative field and 5 years of relevant work experience or 2 years with a Master's degree.
  • Candidates should possess strong analytical skills and experience with coding in Python, R, or SQL, ideally in the payments or ecommerce industry.
  • The role involves shaping Payments products, conducting analyses, managing multiple projects, and leveraging Gen AI tools for data exploration and workflow automation.
  • The salary range for this position is between $138,000 and $197,000, along with a 15% bonus target and equity options.
  • Successful candidates will provide analytical leadership, collaborate with cross-product partners, and influence executive decision-making.

Minimum qualifications:

  • Bachelor's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, a related quantitative field, or equivalent practical experience.
  • 5 years of work experience with analysis applications (extracting insights, performing statistical analysis, or solving business problems), and coding (Python, R, SQL) or 2 years of work experience with a Master's degree.

Preferred qualifications:

  • Master's degree in Statistics, Mathematics, Data Science, Engineering, Physics, Economics, or a related quantitative field.
  • Experience working on statistical/casual inference techniques across experimentation and observational studies.
  • Experience working in the payments, online ecommerce, or marketplace industry.

About the job:

As a Data Scientist, you will help shape Payments products, helping our leaders make decisions. You will coordinate across functions to dissect business, product, growth, acquisition, and operational metrics. You will proactively help leverage data, conduct analyses and make final business recommendations, collaborating with Product teams to help build future looking business models. In this role, you will manage multiple projects at a time, focusing on the details and finding creative ways to take on big picture issues.

You will bring an innovation to how analytics is done leveraging Gen AI tools for data exploration and workflow automation while maintaining high standards of experimental design. You will balance multiple high-stakes initiatives, diving into technical details while keeping a sharp eye on the broader goals of both the Identity and Risk organizations. Define and develop comprehensive measurement frameworks and driver trees to evaluate the health and effectiveness of our Risk and IDV solutions, focusing on both system precision and user experience. Provide analytical leadership for key programs, including the evolution of identity assurance and the scaling of sophisticated risk-mitigation infrastructure. Drive high-impact projects that transform product roadmaps, engineering decisions, tracking systems, and control processes across the identity and risk lifecycles. Collaborate with cross-product area partners to align on success metrics and ensure consistent protection strategies across Google’s product portfolio. Your insights and leadership will directly inform executive decision-making, planning, and reporting as you help shape the future of Google Payments during a period of rapid growth.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $138000 - $197000 (USD) + 15% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities:

  • Identify and solve ambiguous, high-stakes problems, transforming data into clear, actionable insights that directly influence leadership decisions (e.g., VPs and Directors).
  • Lead complex projects that combine investigative with organizational strategy, delivering clear and actionable insights that inform tangible business decisions.
  • Conduct end-to-end analysis that includes data gathering and requirements specification, processing, analysis, ongoing deliverables and presentations.
  • Elevate the role of data science within the organization by maintaining high standards of technical excellence, clear communication, and impactful stakeholder influence.



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