Quantitative Developer

Carlyle Group Inc.•Washington, DC

28d•$180,000 - $210,000

About The Position

This role is open to candidates in Washington, DC, and/or New York, NY. The Quantitative Developer role sits at the intersection of software development, quantitative financial modeling, economics research, and portfolio analytics. This role offers the opportunity to design and implement reusable, high-quality analytical software that supports the firm's senior investment professionals and decision-makers globally. This position sits within the Global Technology and Services organization, reporting to the Head of Global Enterprise Solutions. The Quantitative Developer will be functionally embedded with researchers and analysts within Global Research and Investment Strategy (GRIS). As a critical part of Global Research's long-run strategy, they will develop software to maximize the reuse and deployment breadth of current and future research. The primary mission of the Quantitative Developer is to work with researchers, analysts, and technology stakeholders to build long-term scalable tools that empower the decision making of investment professionals, risk managers, and executives.

Requirements

Bachelor's Degree required
Concentration in Information Technology, or similar discipline, strongly preferred
Advanced degree in a quantitative discipline (financial engineering, mathematical finance, STEM, or similar) strongly preferred.
8+ years of relevant IT experience required.
3-6+ years of professional experience in developing software related to quantitative financial models and/or data science in finance, required.
Strong programming experience in using Python for mainstream software development, including packages, API design, testing, code reviews, and version control.
Working knowledge of R, Julia, and/or similar scientific languages as used for quantitative financial models and data analysis.
Familiarity with probability, statistics, time series methods, and the ability to reconcile mathematical model specifications with research and production code.
Experience distilling model output into reports and dashboards for risk and investment professionals.
Excellent communication skills with experience communicating technical concepts to a wide variety of audience sophistications.
A demonstrated history of working with quantitative researchers and/or developers to create re-usable tools for portfolio analytics.
An investor-centric growth-oriented mindset that prioritizes analytics that improve investment decision making and corresponding outcomes for clients.
Excellent technical writing skills and experience partnering with researchers to document complex mathematical models for developers and research colleagues.

Nice To Haves

Experience with data related to alternative investments, including private equity and private credit is a plus.

Responsibilities

Collaborate with researchers and serve as the engineering voice in an iterative process as research progresses from concept to POC to production and support.
Engage in two-way dialogue and feedback with researchers when creating production software, conveying opportunities for enhancements as well as considerations around deployment, reuse, and scalability.
Work closely with researchers and analysts to acquire a conceptual understanding of the underlying research for the purposes of applying and extending models across multiple use cases.
Use research prototypes and models as the basis for well-tested and documented libraries and services. Design and document APIs for shared models to power dashboards, automated reporting, and other tools.
Produce clear technical documentation on models, including taking point on overall documentation structure, working with researchers to produce documentation on math and calculation methodology, and authoring technical documentation on the implementation.
Design, produce, and document automated tests that verify numerical equivalence (within tolerance thresholds) between developed production code with research outputs. Work closely with researchers to review and trace variations and participate in research model review and sign-off.
Fit internal and external team software within the firm's broader enterprise data architecture. Work with technology leaders on data governance for centralized stores of financial data, interfacing between technology and research to develop schemas and document calculation methodologies.
Support researchers and analysts in data and reporting inquiries, identifying and collaborating on automation initiatives.
Engineer packages and service endpoints to maximize reuse and robustness of current and future research artifacts and internal team tools.
Adhere to Carlyle's coding best practices and cybersecurity standards. Participate in Caryle's AI Engineering Community of Practice.