Snap Inc. is a camera company. We believe that reinventing the camera represents our greatest opportunity to improve the way people live and communicate. Our products empower people to express themselves, live in the moment, learn about the world, and have fun together.  

​We’re looking for a (Senior) Applied Data Scientist to join our Monetization Engineering Team. We are looking for a candidate experienced in causal inference and machine learning, and who understands ad technology, auctions and ads marketplace. Working closely alongside engineering, product management, and business product partners, you will be central to creating inventive, data-based approaches to solve difficult business and product problems. 

Applied Data Scientists at Snap design and propose implementable, testable and scalable analytical solutions. We sit at the cross section of data science and machine learning. We provide impactful, objective, and actionable data insights that enable informed engineering and product decisions. We drive informed and timely decision-making that improves and optimizes the way our products are created, executed, and adopted. We deploy solutions with measurable outcomes. 

What you’ll do:

• Partner with the Product and Engineering teams, and apply your expertise in quantitative analysis, data mining, and machine learning to create and scale insights

• Create and test hypotheses

• Build models and machine learning pipelines to understand how Snapchatters interact with ads on Snapchat

• Understand patterns in data to identify key product trends and new product opportunities

• Apply predictive inference methodology to drive engineering growth

Knowledge, Skills & Abilities

• Excellent verbal and written communication skills, with high attention to detail

• Strong statistical knowledge

• Ability to initiate and drive projects to completion with minimal guidance

• Ability to communicate the results of analyses in a clear and effective manner to a senior audience

• Excellent problem-solving skills, and the ability to structure ambiguous business challenges into actionable plans  

Minimum Qualifications:

• M.S. or PhD in CS, Maths, Statistics, Economics, or other quantitative field

• 3+ years of experience in causal inference techniques, experimental design and/or A/B testing

• Fluency in SQL or other big data querying languages

• Experience with programming languages such as Python or R 

Preferred Qualifications:

• 3+ years of experience in machine learning, experience with the entire lifecycle of an ML model, from POC to productionalization

• Experience with Spark and with modern ML frameworks such as tensorflow or pytorch

• Experience in ads measurement or ad tech preferred

• An interest in translating technical results for cross-functional stakeholders across the organization

At Snap, we believe that having a team of diverse backgrounds and voices working together will enable us to create innovative products that improve the way people live and communicate. Snap is proud to be an equal opportunity employer, and committed to providing employment opportunities regardless of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex, gender, gender identity, gender expression, pregnancy, childbirth and breastfeeding, age, sexual orientation, military or veteran status, or any other protected classification, in accordance with applicable federal, state, and local laws. If you have a disability or special need that requires accommodation, please don’t be shy and contact us at accommodations-ext@snap.com. 

Our Benefits: Snap Inc. is its own community, so we’ve got your back! We do our best to make sure you and your loved ones have everything you need to be happy and healthy, on your own terms. Our benefits are built around your needs and include paid maternity & paternity leave, comprehensive medical coverage, emotional and mental health support programs, and compensation packages that let you share in Snap’s long-term success!