Data Science Manager

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.

MaaS (“Money as a Service”) Data Science

The MaaS Data Science team is central to all money movements, embedded finance, and platform solutions for our biggest and most complex customers. The two roles available are in Embedded Finance (Capital, Issuing) and Connect (Stripe’s solution and growth suite for platforms and marketplaces). We set the data foundations for company wide data primitives + external products; drive strategy through modelling, analytics, and experimentation;

build world class data-driven + ML powered product experiences; and partner closely with cross-functional partners across all facets of the business (Product, Engineering, Risk, Marketing, Ops, Strategy, and more) to ensure the success of our Connect, Capital, and Issuing product suite.

Growth Data Science

The Growth Data Science team helps businesses on Stripe get started both quickly and effectively. We work closely with Growth product and engineering leads to optimize every step of the user journey, from awareness and acquisition, through product adoption, to usage growth and retention. Our team is known for being an experimentation powerhouse, for pushing the frontiers of causal analysis and cumulative impact measurement, and for developing a deep understanding of the user journey that helps drive the product roadmap.

We work closely with Growth Product and Engineering, and more broadly with Sales, Marketing, Developer Platform, Support, and Risk teams.

Office-assigned Stripes in most of our locations are currently expected to spend at least 50% of the time in a given month in their local office or with users. This expectation may vary depending on role, team and location. For example, Stripes in our Bucharest, Romania site have an 80% in-office expectation, and those in Stripe Delivery Center roles in Mexico City, Mexico and Bengaluru, India work 100% from the office.

Also, some teams have greater in-office attendance requirements, to appropriately support our users and workflows, which the hiring manager will discuss. This approach helps strike a balance between bringing people together for in-person collaboration and learning from each other, while supporting flexibility when possible.

The annual salary range for this role in the primary location is CA $221,300 - CA $331,900. This range may change if you are hired in another location. 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 specific location.

Applicants interested in this role and who are not located in the primary location may request the annual salary range for their location during the interview process.

Specific benefits and details about what compensation is included in the salary range listed above will vary depending on the applicant’s location and can be discussed in more detail during the interview process. Benefits/additional compensation for this role may include: equity, company bonus or sales commissions/bonuses; retirement plans; health benefits; and wellness stipends.

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.…