Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Data Engineer / Quantitative Modeler - NEW YORK - USA

Park Lane Recruitment
10 months ago
Applications closed

Related Jobs

View all jobs

Senior Quantitative Finance Analyst

Senior Quantitative Finance Analyst

Senior Quantitative Finance Analyst

Data Engineer - Quantitative Trading Frameworks - Hedge Fund

Quantitative Data Engineer

Quantitative Data Engineer

 Data Engineer / Quantitative Modeler
 
- NEW YORK
- USA
 
Work Experience (Years): 2 - 4
 
Salary : $185000 - $225000
 
Other Compensation : Bonus
 
Degree : University - Bachelor's Degree/3-4 Year Degree
 
Remote Status: No Remote
 
Client Willing to Sponsor: Yes
 
Relocation Paid: Yes
 
Industry(ies): Information Technology, Professional Services, Real Estate/ Mortgage, Research & Development Services
 
Primary Skills: Python
 
Occupational Categories: Accounting/ Financial Services/ Investing
 
Job Description :
Quantitative Modeler / Data Engineer :
We are looking for a highly motivated and talented Quantitative Software Developer / Credit Modeler with a strong academic background and a passion for data, machine learning and the desire to join a strong, collaborative team. 10 Billion AUM Hedge Fund, our client is committed to developing state of the art models and technology, driving our investment and risk management decision making processes. This platform is driven by cutting-edge, cloud-based data & ML solutions.
 
MUST BE FAMILIAR WITH HACKERRANK TESTS.
 
Qualifications:

  • BS in Computer Science, Statistics/Data Science, Mathematics, or Financial Engineering degree from a top university.  MS degree preferred
  • 2-4 years’ experience as a research modeler / quant developer in a hedge fund, asset manager, banking, or fintech environment focused on structured products or consumer credit
  • Proven modeling skills in R and Python.  Experience building loan-level credit / prepayment models through all stages from data preparation, data analysis, model estimation through deployment into production
  • Experience with generalized regression models as well machine learning frameworks
  • Very strong programming and software design skills (Python, C++) required
  • Very strong SQL and DB skills for creating/maintaining necessary tables for data preparation and analysis
  • Excellent communication skills and ability to work collaboratively in a team environment with a flexible, organized, and driven personality
  • Enthusiastic about leveraging models into the firm’s investment process in the structured credit space (RMBS, CMBS, ABS, CLOs)
  • Knowledge of structured products and/or risk management in a fixed-income environment is required
  • Experience creating visualization tools for monitoring or model performance adjustment in a modern JS framework (React, Angular, Vue) is a plus

Responsibilities:

  • This is a hybrid credit modeling / software development role
  • Estimate / develop and enhance credit models in the securitized products (RMBS/CMBS/ABS/CLO) space via data driven credit risk analysis
  • Develop production quality ETL and data integrity processes to build and maintain credit models
  • Create visual tools for monitoring, back testing and adjusting model performance
  • Develop tools to analyze bid lists, dealer offerings, and new issue deals in the structured credit space with an eye towards automation
  • Collaborate with data scientists, analysts, traders, and other stakeholders to understand requirements and deliver high-quality data solutions

Why is This a Great Opportunity:

  • Competitive salary and benefits package.
  • A dynamic and inclusive work environment with opportunities for professional growth 
  • Access to the latest technologies and tools in the data engineering field
  • Support for continuous learning and career development

 
 
IND123

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Jobs

Data Science Recruitment Trends 2025 (UK): What Job Seekers Need To Know About Today’s Hiring Process

Summary: UK data science hiring has shifted from title‑led CV screens to capability‑driven assessments that emphasise rigorous problem framing, high‑quality analytics & modelling, experiment/causality, production awareness (MLOps), governance/ethics, and measurable product or commercial impact. This guide explains what’s changed, what to expect in interviews & how to prepare—especially for product/data scientists, applied ML scientists, decision scientists, econometricians, growth/marketing analysts, and ML‑adjacent data scientists supporting LLM/AI products. Who this is for: Product/decision/data scientists, applied ML scientists, econometrics & causal inference specialists, experimentation leads, analytics engineers crossing into DS, ML generalists with strong statistics, and data scientists collaborating with platform/MLOps teams in the UK.

Jobs

Why Data Science Careers in the UK Are Becoming More Multidisciplinary

Data science once meant advanced statistics, machine learning models and coding in Python or R. In the UK today, it has become one of the most in-demand professions across sectors — from healthcare to finance, retail to government. But as the field matures, employers now expect more than technical modelling skills. Modern data science is multidisciplinary. It requires not just coding and algorithms, but also legal knowledge, ethical reasoning, psychological insight, linguistic clarity and human-centred design. Data scientists are expected to interpret, communicate and apply data responsibly, with awareness of law, human behaviour and accessibility. In this article, we’ll explore why data science careers in the UK are becoming more multidisciplinary, how these five disciplines intersect with data science, and what job-seekers & employers need to know to succeed in this transformed field.

Jobs

Data Science Team Structures Explained: Who Does What in a Modern Data Science Department

Data science is one of the most in-demand, dynamic, and multidisciplinary areas in the UK tech and business landscape. Organisations from finance, retail, health, government, and beyond are using data to drive decisions, automate processes, personalise services, predict trends, detect fraud, and more. To do that well, companies don’t just need good data scientists; they need teams with clearly defined roles, responsibilities, workflows, collaboration, and governance. If you're aiming for a role in data science or recruiting for one, understanding the structure of a data science department—and who does what—can make all the difference. This article breaks down the key roles, how they interact across the lifecycle of a data science project, what skills and qualifications are typical in the UK, expected salary ranges, challenges, trends, and how to build or grow an effective team.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.