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Title: Data Scientist, Forecasting Platform
Location: US National
About Stripe
Stripe is a financial infrastructure platform for businesses. Millions
of companiesfrom the world’s largest enterprises to the most ambitious startupsuse Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone’s reach while doing the most important work of your career.About the team
At Stripe, you’ll be part of a rich Data Science community for Analysts,
Scientists and Engineers to learn and grow together. At the same time, our embedded org structure means that you’ll be working closely with our Finance and Strategy partner team.What you’ll do
Stripe’s business is complex and growing, and forecasting its future is
no easy feat. Our forecasting efforts are erse, spanning different dimensions of our business (geographies, business types), variable time periods (early-stage vs late-stage users), and methodologies (traditional time series modeling, ML-based methods). We are looking for an experienced data scientist to work on the planning, implementation, and building of infrastructure that enables and automates forecasting across all of Stripe. This role will also work closely with our Finance & Strategy team to forecast our financial metrics. If you are excited about time series modeling and motivated by having an impact on the business, we want to hear from you.- Develop and build a forecasting framework that can produce regular, accurate, responsive statistical forecasts to be used for company planning
- Incorporate new statistical modeling and/or machine learning methods to improve forecast performance
- Drive efforts around explanation of forecast trends, development of new accuracy metrics, and estimation of uncertainty
- Bring in new methodology to improve forecast responsiveness to the macroenvironment, such as COVID and other economic changes
- Build what-if’ analysis capabilities to allow business leaders to quantitatively encode and model their assumptions
Who you are
We’re looking for someone who meets the minimum requirements to be
considered for the role. If you meet these requirements, you are encouraged to apply. The preferred qualifications are a bonus, not a requirement.Minimum Requirements:
- 5+ years experience working with and analyzing large data sets to solve problems
- A PhD or MS in a quantitative field (e.g., Statistics, Sciences, Economics, Engineering, CS)
- Expert knowledge of Python and SQL
- Strong knowledge of statistics and experimental design
- Prior experience working with time series models
- The ability to communicate results clearly and a focus on driving impact
- A demonstrated ability to manage and deliver on multiple projects
- A builder’s mindset with a willingness to question assumptions and conventional wisdom
Preferred qualifications:
- Prior experience with data-distributed tools (Scalding, Spark, Hadoop, etc)
- Prior experience writing or contributing to Python packages
Pay and benefits
The annual US base salary range for this role is $168,600 – $228,229. For sales roles, the range provided is the role’s On Target Earnings (“OTE”) range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role. This salary range may be inclusive of several career levels at Stripe and will be narrowed during the interview process based on a number of factors, including the candidate’s experience, qualifications, and location. Applicants interested in this role and who are not located in the US may request the annual salary range for their location during the interview process.
Additional benefits for this role may include: equity, company bonus or sales commissions/bonuses; 401(k) plan; medical, dental, and vision benefits; and wellness stipends.
Office locations
Toronto
Remote locations
Remote in United States
Team
Data & Data Science
Job type
Full time