# Financial Quantitative Analysts

> Develop quantitative techniques to inform securities investing, equities investing, pricing, or valuation of financial instruments. Develop mathematical or statistical models for risk management, asset optimization, pricing, or relative value analysis.

- **SOC code:** 13-2099.01
- **Canonical URL:** https://singulariki.com/roles/role-13-2099-01
- **Also known as:** Investment Strategist, Portfolio Manager, Quantitative Analyst, Quantitative Equity Analyst, Investment Portfolio Control Consultant, Quantitative Research Analyst, Quantitative Strategy Analyst, Research Analyst
- **Frame:** "AI exposure" means task overlap (how codifiable the work is), not jobs lost or a forecast. Every figure below is traced to a named public dataset.

## What this work is

**Core tasks** (O*NET):
- Apply mathematical or statistical techniques to address practical issues in finance, such as derivative valuation, securities trading, risk management, or financial market regulation.
- Research or develop analytical tools to address issues such as portfolio construction or optimization, performance measurement, attribution, profit and loss measurement, or pricing models.
- Interpret results of financial analysis procedures.
- Develop core analytical capabilities or model libraries, using advanced statistical, quantitative, or econometric techniques.
- Define or recommend model specifications or data collection methods.
- Maintain or modify all financial analytic models in use.
- Produce written summary reports of financial research results.
- Provide application or analytical support to researchers or traders on issues such as valuations or data.
- Devise or apply independent models or tools to help verify results of analytical systems.
- Collaborate in the development or testing of new analytical software to ensure compliance with user requirements, specifications, or scope.
- Confer with other financial engineers or analysts on trading strategies, market dynamics, or trading system performance to inform development of quantitative techniques.
- Consult traders or other financial industry personnel to determine the need for new or improved analytical applications.

## Skills, tools, capabilities

**Knowledge, skills & abilities** (O*NET, highest importance first):
- Mathematics _(knowledge)_
- Mathematics _(essential_skill)_
- Mathematical Reasoning _(ability)_
- Economics and Accounting _(knowledge)_
- Critical Thinking _(essential_skill)_
- Reading Comprehension _(essential_skill)_
- Written Comprehension _(ability)_
- Complex Problem Solving _(transferable_skill)_
- Oral Comprehension _(ability)_
- Written Expression _(ability)_
- Deductive Reasoning _(ability)_
- Number Facility _(ability)_

**Skills in demand:**
- Mathematics _(Common Skill)_
- Critical Thinking _(Common Skill)_
- Reading Comprehension _(Common Skill)_
- Deductive Reasoning _(Common Skill)_
- Complex Problem Solving _(Common Skill)_
- Information Ordering _(Specialized Skill)_
- Inductive Reasoning _(Common Skill)_
- Active Listening _(Common Skill)_
- Active Learning _(Common Skill)_
- Writing _(Common Skill)_
- Speech Recognition _(Specialized Skill)_
- Microsoft PowerPoint _(Common Skill)_

**Tools & technology:**
- C++ _(hot technology, in demand)_
- Microsoft Excel _(hot technology, in demand)_
- Microsoft Office software _(hot technology, in demand)_
- Microsoft PowerPoint _(hot technology, in demand)_
- Microsoft Visual Basic for Applications VBA _(hot technology, in demand)_
- Python _(hot technology, in demand)_
- R _(hot technology, in demand)_
- SAS _(hot technology, in demand)_
- Structured query language SQL _(hot technology, in demand)_
- Tableau _(hot technology, in demand)_
- The MathWorks MATLAB _(hot technology, in demand)_
- Amazon Web Services AWS software _(hot technology)_

## AI exposure & outlook

- **AI task-overlap index:** 94th percentile (High) across all occupations — composite of current-era exposure studies (ai-exposure-index-v1).
- **Overall AI exposure (Felten et al.):** 93rd percentile (High) — source: felten_aioe.
- **LLM task exposure, γ (OpenAI / Eloundou):** 95th percentile (High) — source: eloundou_gamma.
- **AI assistant applicability (Microsoft):** 79th percentile (High) — source: microsoft_applicability.
- **Frey–Osborne (2013, historical computerization estimate):** 40th percentile — kept separate from current-era studies.
- **Remote-capable (Dingel–Neiman):** yes — task structure, not who actually works remote.
- **Projected employment (BLS 2024–34):** 3.1% growth (About average); 10.3k annual openings; 137.1k → 141.4k jobs.
- **Pay & employment (BLS OEWS, May 2024):** median $80,190; 127,450 employed.

## How people actually use AI here

Anthropic Economic Index — measured AI conversations mapped to this occupation's tasks:

- **Automation vs augmentation:** 37% automation, 53% augmentation (usage-weighted).
- **Autonomy median:** 4.0 (higher = AI acts more independently).
- **Dominant collaboration mode:** directive.

**Tasks most handed to AI here:**
- Research or develop analytical tools to address issues such as portfolio construction or optimization, performance measurement, attribution, profit and loss measurement, or pricing models. _(5.3% of measured AI use; directive)_
- Interpret results of financial analysis procedures. _(1.4% of measured AI use; learning)_
- Apply mathematical or statistical techniques to address practical issues in finance, such as derivative valuation, securities trading, risk management, or financial market regulation. _(1.3% of measured AI use; directive)_
- Provide application or analytical support to researchers or traders on issues such as valuations or data. _(0.9% of measured AI use; directive)_
- Produce written summary reports of financial research results. _(0.9% of measured AI use; directive)_
- Prepare requirements documentation for use by software developers. _(0.7% of measured AI use; task iteration)_
- Maintain or modify all financial analytic models in use. _(0.3% of measured AI use)_

**Example prompts (honest phrasings of the tasks above — starting points, not endorsed instructions):**
- Help me research or develop analytical tools to address issues such as portfolio construction or optimization, performance measurement, attribution, profit and loss measurement, or pricing models.
- Help me interpret results of financial analysis procedures.
- Help me apply mathematical or statistical techniques to address practical issues in finance, such as derivative valuation, securities trading, risk management, or financial market regulation.
- Help me provide application or analytical support to researchers or traders on issues such as valuations or data.
- Help me produce written summary reports of financial research results.

## Sources

- **O*NET** (30.3) — U.S. Department of Labor / National Center for O*NET Development. https://www.onetcenter.org/database.html
- **BLS Occupational Employment and Wage Statistics (OEWS)** (May 2024) — U.S. Bureau of Labor Statistics. https://www.bls.gov/oes/
- **BLS Employment Projections** (2024–2034) — U.S. Bureau of Labor Statistics. https://www.bls.gov/emp/
- **Anthropic Economic Index** (v4 (2026-01-15) + v2 (2025-03-27)) — Anthropic. https://www.anthropic.com/economic-index
- **Microsoft “Working with AI”** (working-with-ai) — Microsoft Research. https://www.microsoft.com/en-us/research/
- **“GPTs are GPTs” (Eloundou et al.)** (arXiv 2303.10130) — OpenAI / academic. https://arxiv.org/abs/2303.10130
- **AI Occupational Exposure (AIOE)** (Felten, Raj & Seamans) — academic. https://github.com/AIOE-Data/AIOE
- **Frey & Osborne (2013)** (frey-osborne-automation) — academic. https://www.oxfordmartin.ox.ac.uk/publications/the-future-of-employment/
- **Dingel & Neiman (2020)** (dingel-neiman-workathome) — academic. https://github.com/jdingel/DingelNeiman-workathome

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_Generated from Singulariki's joined dataset; data snapshot 2026-06-02T21:00:32.945303+00:00. https://singulariki.com/roles/role-13-2099-01_