QUANT RESEARCHER - PORTFOLIO CONSTRUCTION

Dualitas Capital Management LLC; New York (NY); On-site; USD 100,000 - 300,000; Full time

Benefits offered by this job

Competitive compensation

Medical/dental/vision coverage

401k with profit sharing

Flexible working arrangement

Work visa and green card sponsorship

Job summary

A financial management firm in New York is seeking a Quant Researcher to conduct research in quantitative portfolio construction. The ideal candidate will have deep knowledge in optimization theories, experience with Python and Java, and a Master’s degree in a quantitative field. The role offers a competitive compensation package, including the possibility of work visa sponsorship. Suitable candidates will also benefit from a collegial and fast-paced work environment with flexible scheduling.

Qualifications

• Deep knowledge in optimization theories, including LP, QP, MIP, SOCP.
• Strong knowledge and experience in advanced machine learning methods.
• Hands-on experience with Python and Java required.

Responsibilities

• Conduct research in quantitative portfolio construction and optimization algorithms.
• Participate in design and maintenance of the portfolio optimization system.
• Develop traditional and non-traditional alpha forecasting techniques.

Skills

Optimization theories

Machine learning methods

Python

Java

Data science tool stacks

Education

Master’s or above in a quantitative discipline

Job description

Overview

QUANT RESEARCHER - PORTFOLIO CONSTRUCTION

Responsibilities

• Conduct research in quantitative portfolio construction and optimization algorithms, primarily for equities portfolios.
• Participate in the design, development and maintenance of the firm’s portfolio optimization system.
• Research advanced feature engineering, selection and combination algorithms.
• Develop traditional and non-traditional alpha forecasting techniques and systems.

Qualifications

• Deep knowledge in optimization theories, including LP, QP, MIP, SOCP, robust optimizations, stochastic optimizations and their applications in quantitative portfolio management, with hands-on experience formulating and solving large-scale portfolio optimization problems.
• Strong knowledge and experience in advanced machine learning methods, tools and their applications in quantitative finance.
• Experience with neural networks (NN) and reinforcement learning (RL) in financial applications is a strong plus.
• Deep understanding of quantitative equities portfolio management process and its key ingredients, e.g., factor models, risk models, market impact models.
• Hands-on experience with Python and Java required, with strong fundamental computer science knowledge, including design patterns, concurrency, threading, algorithms, memory management and data structures.
• Proficient in modern data science tool stacks (e.g., Jupyter, pandas, numpy, scipy, sklearn) with machine learning experience a strong plus.
• Master’s or above degree in a quantitative discipline (e.g., OR, Math, Stats, FE, CS, ECE, Physics, or other related fields).
• Detail oriented and self-motivated.
• Work well within a small team and independently.

What we offer

• A true startup environment: small, collegial, fast-paced, and research-oriented; free of bureaucracy or hierarchy.
• Competitive compensation and benefits packages, including PTO, medical/dental/vision coverage, 401k with profit sharing, and flexible working arrangement (location and schedule wise).
• Full alignment between employees’ career goals and the firm’s growth objectives.
• Work visa and green card sponsorship for candidates who require such.
• The annual base salary range for this role is $100,000-$300,000 (USD) if based in New York, which does not include our comprehensive benefits package or any discretionary bonus compensation this role may be eligible for. Successful candidates’ compensation and benefits will be determined in consideration of various factors.

How to apply

Kindly submit your application through our online application system, or alternatively, send your resume along with any supporting materials, to [e-mail]

If you have any publications (including working papers) and/or open-source projects, in the field of advanced optimizations or statistical learnings and/or their applications, that can potentially be helpful for the evaluations of your applications, please feel free to include either the paper(s) or link(s) to the paper(s) in your application.