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Quantitative Analyst Jobs in California (NOW HIRING)

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Quantitative Analyst, Treasury & Deal Ops

Los Angeles, CA · On-site

$105K - $120K/yr

... quantitative discipline such as mathematics, statistics, or computer science. · 1 to 2 years of hands-on experience preferred · Excellent analytical skills with strong working knowledge of Excel ...

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Quantitative Analyst information

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$55.8K

$132.1K

$236.9K

How much do quantitative analyst jobs pay per year?

As of Sep 3, 2026, the average yearly pay for quantitative analyst in California is $132,124.00, according to ZipRecruiter salary data. Most workers in this role earn between $110,000.00 and $143,600.00 per year, depending on experience, location, and employer.

What is a quantitative analyst?

Quantitative Analysts, often called 'quants,' are professionals who use mathematical models, statistics, and computer programming to analyze financial data and support decision-making in finance. They develop and implement complex models to assess risk, value financial securities, and identify profitable investment opportunities. Quants are commonly employed by investment banks, hedge funds, asset management companies, and other financial institutions. Their work helps optimize trading strategies, manage risk, and improve financial performance.

What does a quantitative analyst do?

The responsibilities of quantitative analysts, or quants, include using mathematical models and statistics to analyze data to assess risks and develop solutions for business issues. In this role, you can work in a variety of industries, from production to finance to insurance. You typically gather and interpret data to help an organization implement a solution for maintaining its fiscal health. Duties vary with the industry. Some positions focus on collecting information from the general public or consumers of particular products through the use of polls and surveys to improve their design and marketing. Other quants work alongside researchers in the health care field to test treatments and medical equipment design.

What are the key skills and qualifications needed to thrive as a quantitative analyst, and why are they important?

To thrive as a Quantitative Analyst, you need a strong background in mathematics, statistics, computer science, and finance, often supported by an advanced degree such as a master's or PhD. Expertise in programming languages like Python, R, or MATLAB, as well as familiarity with financial modeling tools and statistical software, is typically required. Analytical thinking, problem-solving abilities, and clear communication skills help you interpret complex data and convey insights to stakeholders. These competencies are crucial for developing accurate financial models, managing risk, and enabling data-driven decision-making in competitive financial environments.

How does a quantitative analyst typically collaborate with other departments within a financial organization?

Quantitative Analysts frequently work closely with traders, portfolio managers, risk managers, and IT professionals to develop, test, and implement financial models. Effective communication is essential, as they must translate complex quantitative findings into actionable insights for decision-makers. It's common to participate in cross-functional meetings, provide model validation support, and help interpret results for non-technical stakeholders. This collaborative environment fosters both technical skill development and a deeper understanding of the business, which can open doors to broader career opportunities.

What is the difference between Quantitative Analyst vs Data Scientist?

AspectQuantitative AnalystData Scientist
Required CredentialsDegree in finance, mathematics, or statistics; often certifications like CFADegree in computer science, statistics, or related fields; certifications like CAP or data science certifications
Work EnvironmentFinancial firms, investment banks, hedge fundsTech companies, finance, healthcare, and various industries
Employer & Industry UsagePrimarily in finance and investment sectorsAcross multiple industries including tech, healthcare, and retail
Common Search & Comparison IntentUnderstanding roles in finance and investment analysisExploring data analysis and machine learning roles

While both roles involve data analysis and statistical skills, Quantitative Analysts focus on financial modeling and investment strategies within finance firms. Data Scientists have a broader scope, applying data analysis across various industries, often with programming and machine learning expertise.

What is the starting salary of a quantitative analyst?

The starting salary for a quantitative analyst typically ranges from $60,000 to $90,000 annually, depending on factors such as location, education, and industry. Entry-level roles often require strong skills in mathematics, programming, and data analysis tools like Python or R.
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Quantitative Analyst jobs near you
Infographic showing various Quantitative Analyst job openings in California as of August 2026, with employment types broken down into 87% Full Time, 9% Part Time, and 4% Contract. Highlights an 81% Physical, 8% Hybrid, and 11% Remote job distribution, with an average salary of $132,124 per year, or $63.5 per hour.

ASSOCIAT | #23506 Quantitative Research Analyst

Qualitest Group

Santa Clara, CA • On-site

Full-time

Posted 14 days ago

Job description

About the Role
This is a quantitative research role in the buy-side sense of the word. You will be responsible for the alpha content of a live market-signals product: deciding what constitutes a real, tradable signal, proving it with statistics that would survive a due-diligence meeting, and standing behind the numbers when a sophisticated financial client asks how they were produced.
What You Will Do
Own the signal set
  • Make the promote, hold or deprecate decision on every candidate signal produced by the discovery process. A full run evaluates thousands of candidates across taxonomy groupings, markets and horizons - your judgement is the gate between a backtest and a published claim.
  • Interrogate promotion evidence rather than accepting it: rank information coefficient, AUC, directional hit rate, precision at K, temporal stability, and false-discovery-rate-adjusted significance against minimum observation counts.
  • Confirm every promoted signal survives a locked out-of-sample holdout and a placebo battery (shuffled dates, shuffled labels, future-shifted timestamps) before it reaches clients.
  • Separate genuine inverse relationships - negative IC is common and legitimate in risk and geopolitical themes - from artefacts, and confirm sign handling is correct at prediction time.

Backtesting and research design
  • Own the walk-forward backtesting framework in practice: expanding folds, purge and embargo gaps to prevent look-ahead, per-fold aggregation, and combination of evidence across folds. Challenge the design where it is too permissive or too conservative.
  • Design and test compound research hypotheses - multi-factor combinations, sentiment-conditioned filters, geographic constraints, and volatility-regime conditioning.
  • Own the promotion threshold policy. Recommend evidence-backed changes and quantify the false-discovery cost of loosening any criterion.
  • Guard against the classic failure modes: multiple comparisons, survivorship, data leakage from enrichment, and regime-specific overfitting.

Prediction quality and calibration
  • Measure live forecast quality honestly - headline accuracy, accuracy by market and by horizon, Brier score, and reliability curves with expected calibration error.
  • Own the accuracy-versus-coverage trade-off. A high accuracy figure only means something on a defined high-confidence slice; determine and defend the confidence threshold at which the target holds, with the coverage cost stated explicitly.
  • Set and tune the abstention policy - when the model should decline to call a market - balancing selectivity against commercial usefulness.
  • Benchmark against naive baselines (always-neutral, always-long) and refuse to report an edge that does not beat them.

Monitoring and decay
  • Track promoted signals for decay using rolling IC, Z-scores, changepoint detection and slope-change diagnostics; confirm or override automatic deprecations.
  • Maintain the health of the resolution pipeline that converts forecasts into realized outcomes, since every accuracy metric depends on it.

Required
  • Quantitative research experience. 5+ years in a quantitative research, quantitative analyst, systematic strategy or financial data science seat - at a hedge fund, asset manager, proprietary trading firm, bank quant desk, or a financial data or alternative-data provider. You must have owned signal or factor research, not solely implemented someone else's model.
  • Financial markets fluency. Genuine comfort with equity index and ETF return series, forward-return construction, trading horizons, volatility regimes, and macro context. You should be able to look at a signal and form a view on whether the economic story behind it is plausible.
  • Statistical rigour. Working command of hypothesis testing, multiple-comparisons correction (Benjamini-Hochberg or equivalent), rank correlation, ROC/AUC, calibration and proper scoring rules. You should be able to explain what a q-value guarantees that a p-value does not.
  • Time-series discipline. Hands-on experience with walk-forward and purged cross-validation, look-ahead bias prevention, holdout design, and regime-dependent performance.
  • Python as a research tool. Fluent with pandas, NumPy, SciPy, statsmodels and scikit-learn - enough to reproduce, modify and extend research code independently. You are not expected to build production services.
  • SQL. Able to write non-trivial analytical SQL to interrogate signals, forecasts and coverage without waiting on an engineer.
  • Intellectual honesty. A demonstrable track record of killing your own results. This role exists to prevent the publication of a false edge; scepticism has to be a reflex, and it has to survive commercial pressure.
  • Communication. Able to write methodology that stands up to a buy-side reader and explain it verbally to a non-quantitative executive audience.

Preferred
  • Experience with news, sentiment, filings or other alternative-data signals and their particular failure modes.
  • Familiarity with gradient-boosted ensembles (LightGBM, CatBoost, XGBoost), stacking with out-of-fold predictions, and isotonic or Platt calibration.
  • Exposure to conformal prediction, selective-prediction or abstention frameworks, or cost-sensitive decision thresholds.
  • Prior work on a commercial data product where the methodology was client-visible and contractually relevant.
  • Working knowledge of a cloud analytics environment (GCP BigQuery / Vertex AI or equivalent).
  • Graduate degree in statistics, financial engineering, econometrics, mathematics, physics or a comparable quantitative discipline. CFA, CQF or FRM is a plus but not a substitute for research experience.

Benefits:
Why QualityAI?
QualityAI is an AI-first quality engineering company helping enterprises deploy and scale complex systems with greater confidence. Operating across data, models, platforms, infrastructure, and operational environments, the company provides assurance and engineering expertise that helps organizations ensure systems perform reliably in real-world conditions.
Formerly Qualitest, QualityAI supports global enterprises across regulated and technology-driven industries, combining deep engineering heritage with AI-enabled delivery, operational assurance, and lifecycle expertise to help clients achieve certainty at go-live.
  • Be a part of a company who strives to support for diversity and inclusion in the workplace - we are one, we are many at QualityAI. Celebrate culture, share knowledge with engineers from around the globe, and inspire each other through our differences.
  • Local and global opportunities - we offer you internal rotation and international mobility opportunities to grow your career.
  • Clear view of your career and progression with the company - QualityAI is growing massively (since Jan 2021 - added more than 2000 engineers) and giving you the opportunity to grow with us.
  • Never stop experimenting and learning with QualityAI Tech academy: 3000+ training courses, mentorship programs, technical tribes, sponsored certifications, leadership programs and much more.
  • Earn bonuses via our Client Referral and Employee Referral Program's. Refer and earn - tap your network for net-worth.

If you like what you have read, send us your resume and let's start talking!
  • Intrigued to find more about us?
    • Visit our website at https://www.quality-ai.com/
    • LinkedIn: https://www.linkedin.com/company/qualityaigroup

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