Who we are looking for

As a Senior/Staff Data Scientist on Clair’s data team, you’ll be responsible for developing and maintaining critical components of Clair’s loan underwriting engine. The methodologies you develop and apply will directly shape how millions of hourly workers unlock an untapped credit source: their earned wages. We're looking for a Senior/Staff Data Scientist who is excited to manage the tradeoff between accuracy and explainability and build highly predictive models for loan eligibility and limits that are easily understood by hourly workers.

To succeed in this role, you’ll need a deep technical understanding of data science concepts and machine learning. You’ll also need to be comfortable building prototypes of novel methodologies from scratch and deploying models to a production environment. You should also have a depth of knowledge and experience with financial technology and modeling, particularly underwriting, credit risk scoring, and repayment timing optimization. #LI-Hybrid

What you’ll do

• Create new algorithms and predictive models to assess risk and underwrite loans.
• Collaborate with engineering teams to deploy models to a production environment.
• Communicate about algorithms and their business value to technical and non-technical audiences.
• Build supervised and unsupervised learning systems to glean insight from our wide variety of data sources.
• Lead cross-discipline projects fostering collaboration between product, software engineering, data engineering, and data science teams.
• Create prototypes to expose algorithms to stakeholders for gathering research feedback.
• Design research frameworks to capture and evaluate results.

Required Experience/ Qualifications 

• 8 years of work or educational experience in data science and machine learning.
• Proven leadership of data science projects from conception to delivery.
• Proven track record of delivering on products and/or practical research endeavors.
• Experience publishing code to production-level repositories.
• Experience in risk assessment and predictive modeling
• Experience with large transaction-based datasets.
• Experience with Python is required, R is a plus.
• Experience with Snowflake or similar data warehouse systems is a plus.
• Experience with AWS SageMaker, Lambda, and other AWS tooling is a plus.
• Experience with spaCy, NLTK, or HuggingFace libraries is a plus.
• Experience with Tensorflow, Torch, Keras or similar libraries is a plus.
• Experience with a range of open-source and commercially available LLMs is a plus.

Location: NYC metro area with the ability to be onsite in the office 3 days a week