Lead Quantitative Software Engineer / Front-Office Quant Developer, VP

State Street

Confirmed live today High trust

Quick summary

Work type
On-site
Location
Boston, MAPrinceton, NJ
Salary
$120,000–$217,500 / yr
Posted
1 day ago
Freshness
Confirmed live today
Nearby
99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $186k
This role $169k
$108k most similar roles pay here $232k

This role pays less than 70% of similar roles. Most pay $161,062–$211,200 — the shaded band above. At the midpoint, this role pays about $169k versus about $186k for comparable roles.

Based on 240 similar postings.

Employer

About State Street

State Street Corporation is one of the world''s largest custodian banks and asset managers, providing investment servicing, investment management, and investment research to institutional investors. Industry: Financial Services & Asset Custody

State Street currently has 206 open roles on FindRole.

Listed pay typically runs $120,000–$202,500 across 200 roles with salary data.

Most-posted roles

View all roles at State Street

At a glance

TL;DR · Lead Quantitative Software Engineer / Front-Office Quant Developer, VP

Role Posting Log in to save

Lead Quantitative Software Engineer / Front-Office Quant Developer, VP will join the Global Markets IT team to build an in-house XVA application from the ground up. This role involves collaborating with business quant teams to implement features for xva calculations, developing test cases and harnesses, performing algorithm backtesting, and managing runtime infrastructure. The position requires expertise in modern C++ (C++20/23), Java 17+, Python, and CUDA programming. Candidates must navigate complex financial products including Interest Rate Swaps, Swaptions, and Exotic Options while applying quantitative concepts like Monte Carlo Simulations and Hull-White model calibration. Technical requirements include experience with kdb+/q time-series databases, Linux, Boost, QuantLib, and CI/CD pipelines. The role addresses the critical business problem of assessing counterparty specific risk and charges through sophisticated valuation models within the fixed income and rates derivatives domain.

Skills

C++ Java Python kdb+/q SQL CUDA OpenCL Boost QuantLib NumPy Pandas Linux Git CI/CD Monte Carlo Simulations Finite Difference Methods Stochastic Calculus Jira

What you'll do

  • Build state-of-the-art valuation models for XVA calculations using C++, Java, and Python.
  • Implement features and functionality related to xva calculations in collaboration with the business quant team.
  • Develop test cases and test harnesses to validate model implementations.
  • Perform backtesting of algorithms to ensure accuracy.
  • Coordinate with infrastructure teams to set up application compute environments.
  • Deploy and maintain runtime infrastructure while responding to user queries and technical issues.
  • Utilize CUDA programming for high-performance computing tasks.

What we're looking for

  • Experience with diverse technologies including C++, Java, and Python.
  • Proficiency in Modern C++ (C++20/23) and Core Java (17+).
  • Knowledge of financial products such as Fixed Income, Rates Derivatives, and Algorithmic Trading.
  • Familiarity with ancillary tools like kdb+/q, SQL, Linux, Boost, QuantLib, and CI/CD pipelines.
  • Experience with quantitative concepts including Stochastic Calculus, Monte Carlo Simulations, and Hull-White model calibration.
  • At least 15 years of experience.
  • Hands on knowledge of CUDA programming is essential.
  • Master of Science in Financial Engineering or a Bachelor of Science in Computer Science & Mathematics (preferred).

Related searches

More Lead Quantitative Software Engineer jobs Lead Quantitative Software Engineer jobs in Boston All jobs at State Street

More like this

Similar roles

Quantitative Dev/Strat, Systematic Rates Trading

Goldman Sachs

New York, NY 74 days ago $150,000–$225,000
C++ Java Python KDB+/q SQL CI/CD Git Data Structures Algorithms Time-series Databases ETL Pipelines Machine Learning Statistical Modeling Portfolio Optimization Low-latency Systems System Architecture
Save