Data Science Manager

Who we are

About Stripe

Stripe is a financial infrastructure platform for businesses. Millions of companies—from the world’s largest enterprises to the most ambitious startups—use 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 Teams (multiple Roles Available)

Growth Data Science

The Growth Data Science team helps businesses accelerate their journey to accept payments on Stripe, and find the financial products they need to grow their business. We achieve our goals through experimentation, forecasting, attribution modeling, personalization and algorithmic recommendations. We partner with Marketing, Sales and Self-Serve Product teams to develop intelligent data products and insights, and create go-to-market strategies to drive product discovery, adoption and retention.

Developer Experience and Platform Product (DEeP) Data Science

DEeP is a central infrastructure team that builds platforms that enable teams across Stripe to offer a better user experience. The DEeP Data Science team provides data, metrics and insights to improve key features of the Stripe user experience including the Stripe Dashboard, developer tools, API policies, Stripe Apps, and more. DEeP DS works closely with other functions to build scalable data solutions, inform strategy, provide observability, and run experiments.

What you’ll do

Data Science Managers at Stripe are responsible for the success of their team. You’ll be deeply involved in the modeling and design processes as well as coaching, mentoring, and leading the team. You’ll have a deep understanding of how to drive efficient data science teams and you’ll have a strong user-focus. You’ll be working with data scientists, analysts and engineers on creating technical solutions and communicating effectively across teams and senior leadership.

Responsibilities

• Drive the roadmap and priorities for your team, and work with many Stripe leaders across the company to enhance our ability to be data driven.
• Collaborate with stakeholders across the organization such as engineering, analytics, operations, finance, and marketing.
• Lead and manage processes to help the team do its best work and engage effectively with the rest of Stripe
• Manage a high-performing team of data scientists, supporting them to achieve a high level of technical excellence and advance in their careers.
• Recruit and onboard great data scientists, in collaboration with Stripe’s recruiting team
• Contribute to broad data science initiatives as a member of Stripe’s data science management team.
  
  
  

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

• You have at least 3 years of direct management experience leading data science and ML teams, and 10 years of overall data science experience.
• You have demonstrated expertise in designing metrics and guiding business decisions with data.
• You have technical expertise to drive clarity with staff and senior scientists about architecture and strategic modeling decisions.
• You’ve managed teams that have built and shipped machine learning systems and data products at scale, and have hands-on experience with challenging problems.
• You work very well cross-functionally, and are able to think rigorously and make hard decisions and tradeoffs.
• You have clear and persuasive communication skills in writing and verbally.
• You thrive on a high level of autonomy and responsibility.
• You foster a healthy, inclusive, challenging, and supportive work environment.
  
  
  

Preferred Qualifications

• A PhD or MS in a quantitative field (e.g., Statistics, Operations Research, Economics, Computer Science, Engineering).
• You are comfortable working with geographically distributed teams
• Expertise in time series forecasting, predictive modeling, or optimization
• Expertise in data design and building scalable data architectures
  
  
  

Hybrid work at Stripe

This role is available either in an office or a remote location (typically, 35+ miles or 56+ km from a Stripe office).

Office-assigned Stripes spend at least 50% of the time in a given month in their local office or with users. This hits a balance between bringing people together for in-person collaboration and learning from each other, while supporting flexibility about how to do this in a way that makes sense for individuals and their teams.

A remote location, in most cases, is defined as being 35 miles (56 kilometers) or more from one of our offices. While you would be welcome to come into the office for team/business meetings, on-sites, meet-ups, and events, our expectation is you would regularly work from home rather than a Stripe office. Stripe does not cover the cost of relocating to a remote location. We encourage you to apply for roles that match the location where you currently or plan to live.

Pay and benefits

The annual US base salary range for this role is $207,800 - $311,600. 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.