Quantitative Developer - Structured Credit Technology Team
About The Position
About Davidson Kempner Davidson Kempner Capital Management LP is a global investment management firm. Founded in 1983, Davidson Kempner is headquartered in New York and has offices in Philadelphia, London, Dublin, Hong Kong, Shenzhen, Mumbai and Abu Dhabi. Our Firm invests globally and opportunistically across the capital structure, in a variety of credit and equity strategies as well as real assets. We apply our multi-dimensional, research-driven investment process to evaluate and execute a diverse range of transactions across asset classes, geographies and market cycles. We target complex, global situations where our experience and expertise can unlock value. We bring together exceptional people from different disciplines and backgrounds who are energized by the challenges of navigating complexity. We look for people who demonstrate exceptional critical thinking skills, innate curiosity, creativity and embrace diverse viewpoints to calibrate their decisions. These differentiators make our people successful beyond a specific job at Davidson Kempner – but throughout their journey with us over many years. Quantitative Developer - Structured Credit Technology Team We are seeking passionate and creative quantitative developers who are eager to build innovative products and embrace new technologies. As part of our small, agile, and highly skilled team of technologists, you will contribute to building next-generation platforms to optimize investment decisions. Our team is extremely delivery-focused, curious, proactive, and action-oriented. As a Quantitative Developer in the Structured Credit Technology Team, you will design, develop, and maintain high-quality, scalable applications and analytics tools that support the structured credit investment team. You will collaborate closely with portfolio managers and quantitative researchers to translate investment and risk management needs into robust technical solutions. Responsibilities include building and maintaining data pipelines and pricing libraries to enable structured credit analytics and trade lifecycle management, as well as developing models for pricing, risk analysis, and performance attribution across products such as ABS, CLOs, RMBS, CMBS, and other credit-linked instruments. You will manage large datasets from multiple sources, performing data wrangling, transformation, and visualization to support modeling, research, and reporting, while addressing data quality challenges such as outliers, missing data, and inconsistent structures. Additionally, you will implement best practices in software development, testing, version control, and deployment. This role requires close collaboration with risk management, data strategy, and front-office users to understand business problems, articulate solutions using cutting-edge technologies, and execute on that vision, while creating synergies across the Investment Technology organization. Our most competitive candidates will have: 2+ years of experience developing pricing or risk software at banks or asset management Experience developing quantitative pricing libraries (QuantLib), developing risk models or Experience with python ecosystem, big data and cloud Experience with DataBricks is a Distributed grid computing Experience with service based systems Experience with risk analytics and financial products (equities, derivates, fixed income). A Bachelor’s or Master’s degree in Computer Science, mathematics, financial engineering or related engineering discipline. We do not expect new grads to have acquired all these skills yet, but it does provide good context to our opportunity. The idea candidate will demonstrate the following expected skills and behaviors: Delivers Delivers Results Is reliable and delivers consistent, accurate, organized and client-ready work in a timely manner with a sense of urgency; is able to expertly multi-task without sacrificing a high standard of work product and output; identifies ways to improve products / processes / procedures to drive efficiencies and output Innovates: Consistently looks for better solutions versus acceptance of existing processes; Identifies and introduces new perspectives to drive change; is open to diverse viewpoints; promotes cutting- edge thinking and solutions; encourages adoption of best- practices Connects Builds Relationships: Is considered a valued, respectful and inclusive partner to stakeholders; proactively manages strong internal / external partnerships and identifies opportunities to build and strengthen relationships; contributes and celebrates the Firm's and others' success Influences for Outcome: Understands what's important, including preference, value and motivation, to the person or people being influenced; listens to find common ground, and tailors the message to articulate "what's in it for them" Leads Leads by Example with Team Mentality: Exhibits strong work ethic and sets example for others; is enthusiastic and optimistic; is accommodating of others and prioritizes team goals while seeking opportunities to improve cohesion and to celebrate team success; seeks opportunities to continuously learn and build skills Engages with Feedback: Seeks ways to continually develop self and others; contributes to the development of others through honest, real time and constructive feedback
Requirements
- 2+ years of experience developing pricing or risk software at banks or asset management
- Experience developing quantitative pricing libraries (QuantLib), developing risk models or
- Experience with python ecosystem, big data and cloud
- Experience with DataBricks is a
- Distributed grid computing
- Experience with service based systems
- Experience with risk analytics and financial products (equities, derivates, fixed income).
- A Bachelor’s or Master’s degree in Computer Science, mathematics, financial engineering or related engineering discipline.
Responsibilities
- design, develop, and maintain high-quality, scalable applications and analytics tools that support the structured credit investment team
- collaborate closely with portfolio managers and quantitative researchers to translate investment and risk management needs into robust technical solutions
- building and maintaining data pipelines and pricing libraries to enable structured credit analytics and trade lifecycle management
- developing models for pricing, risk analysis, and performance attribution across products such as ABS, CLOs, RMBS, CMBS, and other credit-linked instruments
- manage large datasets from multiple sources, performing data wrangling, transformation, and visualization to support modeling, research, and reporting, while addressing data quality challenges such as outliers, missing data, and inconsistent structures
- implement best practices in software development, testing, version control, and deployment
- close collaboration with risk management, data strategy, and front-office users to understand business problems, articulate solutions using cutting-edge technologies, and execute on that vision, while creating synergies across the Investment Technology organization
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
