Pizza is at the heart of our communities. From birthday parties to gameday potlucks, life’s special moments are bettered by the craftsmanship and tradition found behind local pizzerias’ counters. We’re here to make sure these iconic small businesses serve our communities for generations to come by giving them the digital tools and services commonly found at big chains. Can you imagine what a small mom and pop pizza shop could achieve with the resources of Domino’s?

We are looking for a seasoned Data Scientist with significant experience in revenue management related projects, applicable industry-standard statistical methods, related experimentation and measurement and data storytelling to join the Business Analytics and Data Science team at Slice. 

The Business Analytics and Data Science team is a partner to the Sales, Marketing, Go-to-Market, and other strategic business departments. We play an active role in establishing data-driven business decision making capabilities, identifying new business opportunities and measuring impact of our business initiatives.

We are a rapidly growing team that operates with an ownership mind, believes in driving towards our business goals with a great degree of collaboration and looks to empower the team members to drive outsized impact. This role will report to the Director of Business Analytics and Data Science and lead a team of up to 2 people.

What you’ll do:

• Build models (statistical, optimization, econometric and/or ML) to inform business decisions including pricing, targeting, LTV and business forecasting, and translate findings into action recommendations.
• Turn data science prototypes into business-facing solutions, in close partnership with data engineers and business stakeholders.
• Work closely with multi-functional leads to develop technical vision and drive alignment on horizontal/cross-functional initiatives
• Build and grow a high performing Revenue Analytics team.

This role will report to the Director of Business Analytics and Data Science.

Who you are:

• Ph.D or MS in statistics, operations research, mathematics or related quantitative fields
• 6+ years of experience in leading predictive modeling efforts around pricing, business forecasting and/or LTV, preferably at a large, well-established b2c technology company.
• Experience in experimental design and analysis (A/B, market-level) and causal inference
• Proficiency in SQL and experience working with large database systems
• Experience in running advanced analyses in languages such as Python, R, etc. Experience with model production and MLOps practices preferred.
• Interest in and ability to provide technical leadership to a small team
• A proven track record of using analysis to impact key business decisions
• The ability to clearly and effectively communicate the results of complex analyses

About Slice:

Slice powers independent pizzerias with the specialized technology, data insights, and shared services they need to serve today’s digital-minded customers. This united network of pizzerias enables these small businesses to thrive against major corporate chains and form the nation’s largest marketplace for authentic pizza. Slice makes it easy for customers to order from their go-to shops and discover their next favorite. 

Serial tech entrepreneur Ilir Sela started Slice to solve the digital challenges his family’s New York City pizzerias faced. Today, the Slice team has grown to over 700+ across 5 offices globally. If you’re ambitious, interested in growing your career, and hungry to join one of the fastest growing companies in tech, we may have a role for you. Check out a few awards we’ve recently won for our workplace and culture: Inc., Crain's, BuiltinNYC

Slice is an Equal Opportunity Employer and is committed to building an inclusive environment for people of all backgrounds and everyone is encouraged to apply. We do not discriminate on the basis of race, color, gender, sexual orientation, gender identity or expression, religion, disability, national origin, protected veteran status, age, or any other status protected by applicable national, federal, state, or local law.