Staff Data Scientist, Risk Analytics Models

Sorry, this job was removed at 12:19 a.m. (CST) on Wednesday, Jul 31, 2024
Be an Early Applicant
New York, NY
Hybrid
5-7 Years Experience
Fintech • Mobile • Software • Financial Services
SoFi’s mission is to help people reach financial independence to realize their ambitions.
The Role



Shape a brighter financial future with us.
Together with our members, we're changing the way people think about and interact with personal finance.
We're a next-generation financial services company and national bank using innovative, mobile-first technology to help our millions of members reach their goals. The industry is going through an unprecedented transformation, and we're at the forefront. We're proud to come to work every day knowing that what we do has a direct impact on people's lives, with our core values guiding us every step of the way.

We are looking for a Staff Data Scientist to join our Risk Analytics Modeling Team within Risk Analytics. This team member's responsibilities include model development and performance monitoring supporting data-driven decision-making within our second line of defense. The Staff Data Scientist will play a key role in developing loss forecasting and CECL models across various SoFi products including but not limited to Personal Loans, Student Loans and Credit Cards. The Staff Data Scientist will contribute to the performance analysis of SoFi products using empirical measurements, develop quantitative and machine learning models to forecast losses and provide insights on the drivers for losses. She/He will also collaborate with the Business Unit, Finance, Accounting, Credit & Fraud Risk groups. This position requires knowledge of data analytics and modeling using Python and machine learning/analytical packages as well as strong problem solving and communication skills. The ideal candidate should have hands-on knowledge on common loss forecasting methodologies (e.g. econometrics modeling, survival modeling, state transition, Markov Chain etc.) and excellent knowledge of data science, statistical methodologies and machine learning models (e.g. linear regression, logistic regression, decision trees, gradient boosting, random forests, neural network, clustering analysis etc.).
By joining SoFi, you'll become part of a forward-thinking company that is transforming financial services for the better. We offer the excitement of a rapidly growing startup with the stability of an industry leading leadership team.

The Staff Data Scientist will help SoFi develop better data driven modeling solutions by:

  • Developing quantitative/machine learning models to forecast product losses
  • Aggregating and synthesizing datasets from multiple data environments
  • Analyzing complex datasets to understand the performance and drivers for losses across various products
  • Investigating external credit data to identify trends in the market and industry
  • Conducting loss sensitivity analysis
  • Automating models and analytical dashboards
  • Monitoring the models' performance and re-calibrating the models as needed
  • Working with Business Units, Operations, Product, Capital Markets, Finance, Accounting and Risk partners to ensure correct loss expectations and trend of losses are communicated effectively and executed appropriately



  • 6+ years of loss forecasting experience with a Master's or PhD degree in Statistics, Mathematics, Economics, Engineering, Computer Science, or a quantitative field
  • Proficient in Python, SQL & Tableau
  • Experienced in model development and data analysis
  • Excellent knowledge of data science, statistical methodologies and machine learning models, e.g. linear regression, logistic regression, decision trees, gradient boosting, random forests, neural network, clustering analysis etc.
  • Hands-on knowledge on common loss forecasting methodologies, e.g. econometrics modeling, discrete survival modeling, state transition, Markov Chain etc.
  • Strong communications and presentation skills
  • Someone who is highly motivated and drives change, is eager to learn and able to work collaboratively in a complex and fluid environment



  • Familiarity working with bureau sandbox data a plus
  • Experience with generating credit reporting dashboard a plus
  • Experience with developing and productionizing models in the AWS environment a plus



The base pay range for this role is listed below. Final base pay offer will be determined based on individual factors such as the candidate's experience, skills, and location.
To view all of our comprehensive and competitive benefits, visit our page!
SoFi provides equal employment opportunities (EEO) to all employees and applicants for employment without regard to race, color, religion (including religious dress and grooming practices), sex (including pregnancy, childbirth and related medical conditions, breastfeeding, and conditions related to breastfeeding), gender, gender identity, gender expression, national origin, ancestry, age (40 or over), physical or medical disability, medical condition, marital status, registered domestic partner status, sexual orientation, genetic information, military and/or veteran status, or any other basis prohibited by applicable state or federal law.
The Company hires the best qualified candidate for the job, without regard to protected characteristics.
Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.
New York applicants: Notice of Employee Rights
SoFi is committed to embracing diversity. As part of this commitment, SoFi offers reasonable accommodations to candidates with physical or mental disabilities. If you need accommodations to participate in the job application or interview process, please let your recruiter know or email [email protected].
Due to insurance coverage issues, we are unable to accommodate remote work from Hawaii or Alaska at this time.

If you are a current employee, do not apply here - please navigate to our Internal Job Board in Greenhouse to apply to our open roles.

What the Team is Saying

Kari
Niyati
Mike
Suban
Caroline
Mona
Donald
Kari
Yelena
Thomas
The Company
HQ: San Francisco, CA
4,500 Employees
Hybrid Workplace
Year Founded: 2011

What We Do

For over a decade, SoFi has helped transform the Fintech industry by creating financial products and services that help people borrow, save, spend, invest, and protect their money better, so they can achieve financial independence and realize their ambitions. Whether it’s owning a home, saving for retirement, paying off their student loans, or helping our members invest - SoFi is there every step of the way. Want to learn more about how it works? Check it out here: https://www.sofi.com/how-it-works/

Our core values are at the center of how we help our millions of members get their money right. They are our guiding principles for how we think about serving our members, building our company, and most importantly, how we work together. At SoFi, it’s not just what we do - but how we do it.

SoFi is also proud to be the naming rights partner of SoFi Stadium, home of the Los Angeles Chargers and the Los Angeles Rams.

For more information, visit SoFi.com

Why Work With Us

Together with our members, we’re changing the way people think about and interact with personal finance. We’re a next-generation Fintech company using innovative, mobile-first technology to help our members reach their goals. The industry is going through an unprecedented transformation, and we’re at the forefront.

Gallery

Gallery
Gallery
Gallery
Gallery
Gallery

SoFi Offices

Hybrid Workspace

Employees engage in a combination of remote and on-site work.

For the majority of our workforce who work on a hybrid schedule, the in-office requirement is a handful of days per month!

Typical time on-site: Flexible
Company Office Image
HQSan Francisco, CA
Company Office Image
Frisco, TX
Company Office Image
Helena, MT
Company Office Image
Jacksonville, FL
Company Office Image
New York, NY
Company Office Image
Salt Lake City, UT
Company Office Image
Seattle, WA
Learn more

Sign up now Access later

Create Free Account

Please log in or sign up to report this job.

Create Free Account