Company Background
FirstKey Mortgage, LLC ("FKM") is one of the world’s leading private label securitization and asset management firms. We are a boutique financial services company with a primary focus on the buying and securitizing of residential mortgage and consumer loans. Since inception, FKM has excelled in supporting loan acquisitions, securitizing, and managing real estate and other related assets in the U.S. and Europe.
Established in 2013, FKM is a portfolio company of Cerberus Capital Management and has participated on over $80+ billion rated securitization transactions across 85 bespoke ABS/MBS deals globally.
FKM employs approximately 45 mortgage banking professionals and is headquartered at 900 Third Avenue in midtown Manhattan. Our officers and directors have an average of 20+ years industry experience.
FKM strives for business excellence and superior execution with the following critical functions:
- Managing the loan bidding processes which includes data mapping and ingestion, loan payment history analysis and detailed communication with multiple counterparties.
- Efficient loan document review using machine learning and optical character recognition.
- Vetting of loans for any issues with lending laws, taxes, or underlying collateral value.
- Payment collection, surveillance, and loss mitigation once the loan is purchased.
- Securitization of loans into bonds and marketing these assets to institutional investors.
Job Description and Responsibilities
The Data Scientist will support the strategic use of data to drive well-informed business decisions. Reporting to the Head of Research and Analytics, this role focuses on translating complex real estate data into actionable insights, building predictive models, and developing analytical tools that support investment, operations, and market strategy. The ideal candidate combines strong technical expertise in data science with an understanding of real estate market dynamics.
- Collect, clean, and analyze market, resident and property datasets to support business strategy.
- Conduct geospatial analyses to identify market trends and high-potential investment opportunities.
- Build and maintain predictive models to forecast property values, rental yields, and investment risks.
- Develop dashboards and visualizations to communicate insights effectively to stakeholders.
- Monitor and refine analytical models to maintain accuracy and relevance over time.
- Stay current on real estate trends and emerging data science methodologies to enhance analytics capabilities.
The duties and responsibilities described here are not exhaustive and additional assignments, duties, or responsibilities may be required of this position. Assignments, duties, and responsibilities may be changed at any time, with or without notice, by FKM in its sole discretion.
Qualifications
FKM seeks to hire individuals who are highly motivated, intelligent and have demonstrated excellence in prior roles. The successful candidate should have:
- Bachelor’s degree in quantitative field such as Statistics, Mathematics, Computer Science or Engineering
- 3–5 years of experience in data analysis, data science, or analytics, preferably in real estate.
- Strong proficiency in programming languages such as Python, R, and SQL; experience with CoreLogic, MLS, HMDA and transaction data strongly preferred
- Exceptional analytical skills with strong attention to detail and the ability to translate data into actionable insights.
- Proactive problem-solver with effective communication skills and the ability to manage multiple priorities.
Compensation
The base salary for this position is expected to be between $125,000 and $175,000 annually. The base salary offered to the chosen candidate will be commensurate with a candidate’s relevant experience and other qualifications for the position, as determined by FKM in its sole discretion. In addition to base salary, this position is eligible for an annual discretionary bonus, which is often a meaningful portion of the compensation package, and a comprehensive benefits package.
