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Quantitative Portfolio Optimization information

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$98K

$169.7K

$259.5K

How much do quantitative portfolio optimization jobs pay per year?

As of Jul 25, 2026, the average yearly pay for quantitative portfolio optimization in the United States is $169,729.00, according to ZipRecruiter salary data. Most workers in this role earn between $134,500.00 and $199,000.00 per year, depending on experience, location, and employer.

What is quantitative portfolio optimization?

Quantitative portfolio optimization is the process of using mathematical models and statistical techniques to construct investment portfolios that maximize expected returns for a given level of risk, or minimize risk for a given level of expected return. This approach relies on quantitative data analysis, such as historical prices, returns, and correlations between assets, to inform the portfolio construction process. Techniques like mean-variance optimization, factor models, and Monte Carlo simulations are commonly used. The goal is to systematically select asset weights that best align with an investor's financial objectives and risk tolerance.

What are the key skills and qualifications needed to thrive as a Quantitative Portfolio Optimization specialist, and why are they important?

To thrive in Quantitative Portfolio Optimization, you need strong quantitative analysis skills, advanced knowledge of statistics and finance, and typically a degree in mathematics, finance, or a related field. Expertise in programming languages like Python, R, or MATLAB, and familiarity with financial modeling tools and data analytics platforms are commonly required. Attention to detail, critical thinking, and effective communication are essential soft skills for interpreting data and collaborating with stakeholders. These skills ensure the development of robust, data-driven investment strategies that maximize returns and minimize risk.

What are some common challenges faced by professionals in Quantitative Portfolio Optimization, and how can they be addressed?

Professionals in Quantitative Portfolio Optimization often encounter challenges such as managing large and complex datasets, adapting to rapidly changing market conditions, and balancing risk versus return in real time. Staying current with the latest financial models and programming techniques is essential, as is collaborating closely with portfolio managers and data scientists to ensure that optimization strategies align with investment goals. Building strong analytical and communication skills can help address these challenges and contribute to more effective decision-making within the team.

What is the difference between Quantitative Portfolio Optimization vs Quantitative Analyst?

AspectQuantitative Portfolio OptimizationQuantitative Analyst
Primary FocusDeveloping models to optimize investment portfolios for risk and returnAnalyzing data to support investment decisions and strategy
Skills & CertificationsMathematics, statistics, programming, finance certifications (CFA, FRM)Statistics, programming, finance knowledge, often CFA or similar
Work EnvironmentQuant teams within asset management or hedge fundsInvestment banks, asset managers, hedge funds
GoalsMaximize portfolio performance while managing riskProvide insights and analysis to inform investment strategies

While both roles require strong quantitative skills and finance knowledge, Quantitative Portfolio Optimization focuses specifically on creating models to optimize investment portfolios, whereas a Quantitative Analyst provides broader data analysis and insights to support investment decisions.

More about Quantitative Portfolio Optimization jobs
What job categories do people searching Quantitative Portfolio Optimization jobs look for? The top searched job categories for Quantitative Portfolio Optimization jobs are:
Infographic showing various Quantitative Portfolio Optimization job openings in the United States as of July 2026, with employment types broken down into 88% Full Time, 10% Part Time, and 2% Contract. Highlights an 80% Physical, 5% Hybrid, and 15% Remote job distribution, with an average salary of $169,729 per year, or $81.6 per hour.
Experienced Quantitative Portfolio Manager or Strategist NY

Experienced Quantitative Portfolio Manager or Strategist NY

Kershner Trading Group

Manhattan, NY • On-site

$134K - $173K/yr

Full-time

Posted yesterday

Job description

Kershner Trading Group and SMB Capital, a joint venture of leading proprietary trading
and technology firms with offices in New York, Austin, and Chicago, are seeking
Experienced Quantitative Portfolio Managers / Strategists for the U.S. equity market and
Crypto currency.
Kershner Trading Group / SMB Capital is a collaborative research environment and is
seeking individuals with a strong entrepreneurial spirit, exceptional work ethic, and
strong analytical skills to develop new trading strategies. The firm provides a cutting
edge data platform, high performance elastic research and trading infrastructure,
investment capital and trader coaching/support. We provide access to rich datasets
(e.g., tick data, fundamental datasets, sentiment and other alternative datasets), a state-
of-the-art research environment ideal for machine learning, integrated simulation and
production environments with co-located execution engines and advanced risk
management and monitoring tools.
Ideal candidates will have an MS or PhD in an Engineering or Pure Science discipline
with expertise in alpha research, portfolio construction, risk management and trade
execution. Relevant quantitative skill sets include Artificial Intelligence, Machine
Learning, Natural Language Processing, Portfolio Optimization, Linear Programming,
Time Series Prediction, Factor Analysis and/or Fundamental Equity
Valuation. Candidates should have a proficiency in one of the following programming
languages: Python (preferred) and/or C++, C#, Java or R. Candidate should have
recent track record or demonstrate a direct contribution to profitable systematic trading
strategies or process in U.S. Equities and cryptos. Intraday strategies and medium to
high frequency are preferred. Experience with futures, FX and international equity
trading is also a plus. Candidates should have the ability to deploy and manage trading
strategies from inception.
Opportunities are available in the New York office with some options available for
remote teams and team members.

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