- Experience
- Up to 2 yrs
- Salary
- USD 141,500 – USD 196,000 / year
- Openings
- 1
- Posted
- vor 4 Stunden
- Work mode
- Work from home
- Education
- Graduate degree in quantitative field
- Resume
- Required to apply
Job description
About the Company
Green Velvet operates with a mission to significantly reduce the complexity and cost of borrowing for all Americans. Harnessing creativity, experimentation, and cutting-edge AI, the company aims to expand access to fair and intelligent credit decisions through a digital-first platform that powers over one million borrower predictions daily using extensive data signals.
Role Overview
The Applied Scientist position is part of the Unsecured Underwriting Machine Learning team, which builds and refines models that shape credit decisions for unsecured lending products. The individual will conduct research and develop improvements on core unsecured underwriting models, influencing decisions while balancing innovation with rigorous evaluation and responsible deployment.
Key Responsibilities
- Investigate machine learning and statistical methods to enhance predictive abilities of underwriting models.
- Create, test, and assess model improvements using structured experimentation and validation techniques.
- Examine model outcomes and impact to ensure reliable and improved credit decisioning.
- Collaborate with engineering teams for technical assessments, implementation, and deployment tasks.
- Communicate research findings clearly and translate them into well-documented, production-ready solutions.
Minimum Qualifications
- Graduate degree in a quantitative discipline such as mathematics, statistics, physics, econometrics, operations research, or computer science.
- 0 to 2 years of experience applying machine learning, statistical modeling, or related quantitative research within academic or professional environments.
- Skill in developing and evaluating machine learning or statistical models using Python.
- Strong grounding in probability, statistics, and machine learning concepts.
- Experience designing experiments or validation approaches to measure model accuracy and effectiveness.
Preferred Qualifications
- Doctoral degree (PhD) in a quantitative field.
- Familiarity with supervised learning, feature engineering, and model evaluation methods.
- Experience handling large or complex datasets and converting analytical concepts into implemented solutions.
- Capability to maintain a balance between fast research progress and maintaining model dependability and ethical standards.
- Excellent verbal and written communication skills enabling effective collaboration with cross-functional teams and diverse stakeholders.
Work Location and Travel
This role is remote within the United States or Canada (excluding Quebec). Although primarily remote, occasional in-person onsite meetings are expected, typically once or twice per quarter, lasting 2-4 consecutive days for collaborative sessions.
Compensation and Benefits
- Competitive base salary ranging between 141500 and 196000 USD annually, varying by geographic location and experience.
- Performance bonuses, equity grants vesting quarterly, and an employee stock purchase plan where applicable.
- Comprehensive medical, dental, and vision insurance with Health Savings Account contributions for eligible US plans.
- 401(k) retirement plan with up to 100% company matching contributions.
- Life and disability insurance coverage.
- Generous paid vacation, sick, holiday, family, parental, and military leave aligned with local regulations.
- Support programs for family care including fertility and parenthood assistance.
- Employee assistance programs providing mental health and life resources.
- Wellness allowances and ergonomic reimbursements to support health and personal development.
- Team engagement activities, employee resource groups, and regular company-wide updates.
- Office perks including catered lunches and stocked kitchens for those working onsite at company locations.
Equal Opportunity and Accommodations
Green Velvet is committed to inclusive hiring practices and provides reasonable accommodations during the recruitment process upon request.