Senior Data Scientist at Kikoff

San Francisco, California, United States -

Full Time

Start Date

Immediate

Expiry Date

10 Jul, 26

Salary

254000.0

Posted On

11 Apr, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

SQL, Python, Statistical Analysis, Experimentation Design, Machine Learning, Causal Inference, Data Storytelling, Product Development, Analytical Mindset, Product Intuition, Cross-functional Collaboration, Data Engineering, Marketing Analytics, Financial Modeling

Industry

Software Development

Description

Kikoff: A FinTech Unicorn Powering Financial Progress with AI At Kikoff, our mission is to provide radically affordable financial tools to help consumers achieve financial security. We're a profitable, high growth FinTech unicorn serving millions of people, many of whom are building credit or navigating life paycheck to paycheck. With innovative technology and AI, we simplify credit building, reduce debt, and expand access to financial opportunities to those who need them the most. Founded in 2019, Kikoff is headquartered in San Francisco and backed by top-tier VC investors and NBA star Stephen Curry. Why Kikoff: This is a consumer fintech startup, and you will be working with serial entrepreneurs who have built strong consumer brands and innovative products. We value extreme ownership, clear communication, a strong sense of craftsmanship, and the desire to create lasting work and work relationships. Yes, you can build an exciting business AND have real-life real-customer impact. About the Role As a Senior Data Scientist at Kikoff, you work closely with cross functional teams incl. Product, Engineering, Design, Data Engineering, Marketing, Operation. By applying your technical skills, analytical mindset, and product intuition, you will help our customers improve their financial security. You will use data to identify and solve product development’s biggest challenges. Your responsibilities includes: Product leadership: use data to shape product development, identify new opportunities, set goals, quantify upcoming challenges, and ensure the products we build bring value to our customers. Measurement excellence: define what success means to our users and our business, build scalable framework to measure them. Technical ownership: develop hypotheses and employ a diverse toolkit of rigorous analytical approaches, causal inference / ML methodologies, and experimentation best practices to validate them. Communication and influence: convince and influence your partners by telling clear data stories. Qualifications Bachelors’ or above in quantitative discipline: Statistics, Applied Mathematics, Economics, Computer Science, Engineering, or related field A minimum of 5 years of work experience in analytics Experience leading data science for an ambiguous and fast-changing product or technical area, working with senior partners in leadership, product, engineering Expert knowledge of SQL and experience with Python Deep understanding of statistical analysis, experimentation design, common ML and causal inference techniques Excellent verbal and written communication skills A humble collaborative can-do attitude and bias to action Nice to Have Enthusiasm and comfort with using AI in analytics or for fintech problems Experience with Machine Learning especially in a production environment Base Range $226,000—$254,000 USD Equal Employment Opportunity Statement Kikoff Inc. is an equal opportunity employer. We are committed to complying with all federal, state, and local laws providing equal employment opportunities and considers qualified applicants without regard to race, color, religion, creed, gender, national origin, age, disability, veteran status, marital status, pregnancy, sex, gender expression or identity, sexual orientation, citizenship, or any other legally protected class. Please reference the following for more information.

Responsibilities

The Senior Data Scientist will lead product development initiatives by using data to identify opportunities, set goals, and quantify challenges. They will also define success metrics and employ rigorous analytical and machine learning methodologies to validate product hypotheses.