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Data Scientist Sr /Quantitative Analytics Tech Lead

Job

Technology Ventures

McLean, VA (In Person)

Full-Time

Posted 3 days ago (Updated 10 hours ago) • Actively hiring

Expires 7/4/2026

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Job Description

Data Scientist Sr /
Quantitative Analytics Tech Lead Location :
5 days onsite / week
McLean, VA Duration :
6 months contract to Hire Need local to DC metro area who can do onsite interview.
Must Have Qualifications:
Experience with modelling- with at least 3-5 years of experience model testing and model development. Candidates must have a quantitative degree- master s degree required but Doctorate is preferred. The work focuses more on testing and documentation.
Interview Information:
Rounds:
2
ROUNDS Duration:
60 Minuetes|60
Minutes Interview Type:
1st round In Person |2nd round MS Teams -
Video Position Overview:
Quantitative Analytics Tech Lead/Sr to join the Models & Analytics group to develop and enhance mortgage prepayment, default and severity models for portfolio management. The team is responsible for developing models and conducting quantitative analysis to support valuation and hedging of mortgage products.
Qualifications:
Doctorate degree plus 3 years of working experience (or Master's degree with equivalent 5 years of working experience) in statistics, data science or a related quantitative field. 5+ years of relevant experience applying predictive modeling techniques or data analytics to large datasets is preferred. Post-graduate work experience in mortgage valuation and statistical modeling Programming skills in one or more of Python, R, SAS or related languages Exceptional quantitative, empirical analysis, and research skills Experience working with large data sets and relational database Experience in statistical model development and implementation is preferred Keys to Success in this
Role:
Exceptional quantitative, empirical analysis, and research skills Strong knowledge of survival analysis and mortgage valuation Strong programming skills and knowledge in big data analytics such as Apache Spark and AWS cloud computing