Data Scientist, Level 4

What you’ll do

• Apply your expertise in quantitative analysis, data mining, and statistical modeling to deliver impactful, objective, and actionable data insights that enable informed business and product decisions
• Drive informed and timely decision-making that improves and optimizes the way our products are created, executed, and adopted
• Collaborate with product managers, engineers, product marketers, and designers
• Utilize AI tools to more efficiently develop scalable data solutions, while maintaining rigorous standards for accuracy and statistical integrity

• Expertise using data modeling skills to identify key product trends and new product opportunities
• Ability to design, implement, and track core metrics to analyze the performance of our products
• Ability to create visuals, dashboards, and reports to effectively communicate your insights
• Ability to collaborate with engineers, product managers, and other cross-functional teams
• Ability to initiate and drive projects to completion with minimal guidance
• Understanding of Snapchat with great product sense and product understanding
• Adaptability in learning and applying evolving AI systems and tools to remain at the forefront of data‑driven decision-making, insights generation, and industry best practices

• Bachelor's Degree in a relevant technical field such as statistics, mathematics, economics, computer science, engineering or related field or equivalent years of practical work experience
• 3+ years of post‑Bachelor’s experience in quantitative analysis and data science; or a Master’s degree in a technical field + 2+ years of post‑graduate professional experience in quantitative analysis and data science; OR a Ph.D. in a relevant technical field

• 3+ years of experience using SQL or similar big data querying languages
• 3+ years of experience with programming languages such as Python or R
• 3+ years of experience with applied statistical techniques, such as inferential methods, causal methods, A/B testing, or statistical modeling techniques
• Advanced degree in applied mathematics, statistics, actuarial science, economics, or related field
• Experience in a product‑focused role at a social media, online advertising, digital media and/or mobile technology company
• Experience using machine learning and statistical analysis for building data‑driven product solutions or performing methodological research

Snap Inc. values its community and offers a range of benefits to support your happiness and health, including paid parental leave, comprehensive medical coverage, emotional and mental health support programs, and competitive compensation packages.

In the United States, work locations are assigned a pay zone which determines the salary range for the position. The successful candidate’s starting pay will be determined based on job‑related skills, experience, qualifications, work location, and market conditions. The starting pay may be negotiable within the salary range for the position. These pay zones may be modified in the future.

• Zone A (CA, WA, NYC): Base salary range $157,000-$235,000 annually.
• Zone B: Base salary range $149,000-$223,000 annually.
• Zone C: Base salary range $133,000-$200,000 annually.

This position is eligible for equity in the form of RSUs.

Snap is an equal opportunity employer, and is 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.

EOE, including disability/vets. We will consider qualified applicants with criminal histories in a manner consistent with applicable law (by example, the requirements of the San Francisco Fair Chance Ordinance and the Los Angeles Fair Chance Initiative for Hiring, where applicable).