Quantitative Analyst
Mindlance
Fixed Income Risk Model Analyst
Location: Jersey City, NJ - hybrid 3 days a week
Contract Only 2 rounds of Interviews
Primary Responsibilities:
- Maintain and enhance in-house fixed income risk models
- Design and produce model performance metrics and reports to support communications with both internal model users and external supervisors
- Independently format and validate analysis results to ensure quality
Qualifications:
- 5+ years of working experience and must have 3+ years of hands-on experience in quantitative models, research, with deep understanding in fixed income and/or market risk.
- Fluent in at least one high level programming language (Python, C++, Java, etc.). Familiarity with SQL is a plus.
- Knowledge of treasury securities and/or mortgage-backed securities pricing and VaR modeling a big plus
- Strong analytical and problem-solving skills
- Excellent communication skills, both oral and written
- Master's degree or above in a quantitative field of study
Who We Are:
The Government Securities Division (GSD) of the Fixed Income Clearing Corporation (FICC), a subsidiary of DTCC, provides real-time trade matching, clearing, risk management and netting for trades in US Government debt issues, including repurchase agreements or repos. Securities transactions processed by FICC's Government Securities Division include Treasury bills, bonds, notes, zero-coupon securities, government agency securities and inflation-indexed securities.
The Mortgage-Backed Securities Division (MBSD) of the Fixed Income Clearing Corporation (FICC) is the sole provider of automated post-trade comparison, netting, electronic pool notification, pool comparison, pool netting and pool settlement services to the mortgage-backed securities market thus providing greater efficiency, transparency and risk mitigation to this specialized market.
EEO:
"Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of – Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans."
Vacancy posted 2 days ago
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