Fizz is the #1 money app for young adults. Whether you want to build credit, manage your money or have no idea where to start, Fizz is there to help you every step of the way.

Download the app for free to check your credit score, get personalized spending insights and take fun bite-sized courses. Become a Fizz Member to take your finances to the next level with a debit card that builds credit. With no interest and daily autopay that prevents you from overspending, the Fizz Card is the ultimate financial hack. Students at over 300 universities including Michigan State, University of Florida, UCLA and Harvard chose Fizz for safe credit building with daily autopay, smart spend limits, and local campus deals.

Fizz is backed by Kleiner Perkins, SV Angel, Y Combinator, and the founders of eight Unicorns.

Fizz Perks

• High potential for growth & complete ownership from the beginning 📈
• Work with colleagues who get it 🧠
• Rapid learning environment 🚀
• Competitive base salary & equity 💸
• Generous equity in an early-stage startup 🤩
• 100% employer paid medical benefits 🩺
• New York City Office with remote option and flexible WFH policy 💼
• Unlimited vacation policy with 15 official company holidays 🏝
• 401K plan with 4% company match 💪🏻
• 16 weeks of paid parental leave including adoption and fostering

It’s better to apply on Polymer: https://jobs.polymer.co/fizz/36642

This role is open to both part-time and full-time candidates! We are seeking a motivated Data Scientist Intern to join our growing Analytics team for some or all of the school year (and potentially beyond!). The ideal candidate will have some experience in data analysis or related fields, with a foundation in SQL and Python. They should be comfortable working with data pipelines, creating reports, and collaborating with engineers to implement effective tracking solutions. This role offers significant growth potential for someone passionate about leveraging data to drive business decisions in the fintech space.

Qualifications:

1. Proficiency in SQL (understanding of data querying concepts; exact syntax knowledge not required)
2. Knowledge of Python, particularly with data manipulation libraries like Pandas
3. Exposure to BI tools (Looker Studio, Looker, Metabase)
4. Conceptual understanding of the data journey and ELT/ETL processes
5. Understanding of ML and AI algorithms (conceptual knowledge of how they work and how they can be applied to business problems)
6. Proficiency with Cursor/Windsurf and prompt engineering. Passionate about AI
7. Working towards a bachelor's degree in a quantitative field (Computer Science, Statistics, Mathematics, Economics) or equivalent practical experience

Key Responsibilities:

1. Develop and maintain data pipelines using Aribyte, DBT or similar tools
2. Build and maintain dashboards and reports to help teams monitor KPIs and business metrics
3. Analyze user behavior data to identify trends, patterns, and insights that drive product decisions
4. Contribute to the design and optimization of data flows and ETL/ELT processes
5. Conduct ad-hoc analyses to address specific business questions from various departments
6. Support A/B testing initiatives with proper experiment design and result analysis
7. Contribute to data quality efforts by identifying inconsistencies and proposing solutions
8. Identify opportunities to apply ML/AI solutions to business challenges and collaborate with data scientists on implementation

Nice to Have:

1. Knowledge of DBT for data transformation workflows
2. Understanding of marketing technology stacks and attribution models
3. Passion for fintech 
4. Experience with version control systems like Git
5. Familiarity with statistical analysis and hypothesis testing
6. Experience with data visualization techniques and best practices
7. Practical experience implementing or working with ML models for business applications

How to Thrive in This Role:

1. Be curious. Be motivated.
2. Demonstrate intellectual curiosity and a proactive approach to solving problems
3. Communicate data insights clearly to both technical and non-technical stakeholders
4. Balance attention to detail with the ability to move quickly in a startup environment
5. Stay up-to-date with emerging tools and techniques in the data analytics space
6. Contribute to developing a data-driven culture across the organization
7. Learn rapidly and adapt to changing priorities and data needs
8. Think holistically about the entire data lifecycle from collection to analysis

Our backend is built with Node.js/Typescript and our frontend is written with Flutter/Dart.