Lead Data Scientist, Kikoff

$304-469k

+ Stock Options

SQL

Python

Senior and Expert level

San Francisco Bay Area

More information about location

Personal finance & credit building platform

Job no longer available

Personal finance & credit building platform

101-200 employees

B2CCredit ScoringPersonal financeFinancial ServicesSaaS

Job no longer available

$304-469k

+ Stock Options

SQL

Python

Senior and Expert level

San Francisco Bay Area

More information about location

101-200 employees

B2CCredit ScoringPersonal financeFinancial ServicesSaaS

Company mission

To provide refreshingly fair, effective and simple pathways to meet your financial goals.

Job

Company

Role

Who you are

Master's or PhD degree in Physics, Statistics, Mathematics,, Engineering, Computer Science, or a quantitative field
Expert knowledge of data analytics and modeling using SQL, Python and machine learning/analytical packages
Deep understanding and extensive experience in developing and deploying ML solutions from initial concepts to final deliverables
Strong problem solving and communication skills
Self motivated and is eager to learn and able to work collaboratively in a fast moving environment
2+ years of hands-on experience in building credit risk models, preferably within the fintech industry
Attention to details and intellectual curiosity to understand user behavior and loss patterns

What the job involves

You will be in charge of creating an automatic system to make our credit underwriting decisions, working closely with the Product and Engineering. You will develop and keep improving our credit risk models to drive down our loss rate. Your responsibilities includes
Develop machine learning models to predict default
Aggregating and synthesizing datasets from multiple data environments
Search and acquire new datasets
Investigate loss cases to to identify new signal and trends in the user behavior
Conduct loss sensitivity analysis
Build analytical dashboards
Automate model refresh and re-calibrating
Model maintenance including models performance monitoring and drift monitoring
May lead/mentor junior data scientists

Our take

Having a solid credit history is a fundamental building block for financial health and there is a large market of people looking to build or rebuild their credit scores. Roughly half of all American adults have subprime credit or no credit whatsoever, and it’s safe to say there is a demand for credit products tailored to beginners and builders.

Kikoff is a credit-building platform aimed at Millennials and GenZ. Their signature product helps consumers without a credit history to positively influence several key aspects of their credit score. This includes their payment history, how much they owe, and the length of their account history.

Kikoff's goal is to help customers across their entire financial journeys and create a simple and positive banking relationship: from stabilizing cash flows to accumulating savings, and building towards long-term goals such as a mortgage. The company plans to build out its technology and team, across all function areas, to bring new products to market in the future.

Kirsty

Company Specialist at Welcome to the Jungle

Insights

Led by a woman

Top investors

37% employee growth in 12 months

Glassdoor (3.66)

Company

Funding (last 2 of 3 rounds)

Jun 2021

$30m

SERIES B

Jul 2020

$10m

SERIES A

Total funding: $42.5m

Company benefits

Medical, dental, and vision coverage - Kikoff covers the full cost of health insurance!
Stock Options
Generous unlimited vacation policy to help you recharge
Regular team building events to help you get to know the Kikoff team and soo much more!

Company HQ

Financial District, San Francisco, CA

Leadership

Cynthia Chen

(Co-Founder & CEO)

An experienced Chief Revenue/Risk Officer, Cynthia has worked at OnDeck, LendingHome and Figure. Co-founded Figure in 2018, and Kikoff in 2019.

Christophe Chong

(CTO)

Experience as a Software Engineer at Facebook from 2016-2019, and Head of Growth at Lime between 2018-2019. Co-founded Kikoff in 2019.

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