Quantitative Analyst - Research & Analytics Remote

eFinancialCareers; United States; Remote; USD 130,000 - 180,000; Full time

Job summary

eFinancialCareers seeks a Quantitative Analyst to join a fully remote research team focused on market insights from alternative data and rigorous quantitative methods. The role involves signal development, model validation, and systematic evaluation across asset classes in a distributed environment.

The ideal candidate has advanced degrees in quantitative fields and strong Python skills, with experience in time-series, statistics, and large datasets.

Qualifications

• Master’s or PhD in Finance, Economics, Mathematics, Statistics, CS, Engineering, or related quantitative discipline.
• Experience in quantitative research, data science, or analytics within financial markets.
• Proven ability to design, implement, and validate quantitative models using real-world market data.
• Strong proficiency in Python for research and modelling (pandas, numpy, scipy, statsmodels, scikit-learn).
• Experience with large datasets and databases (SQL and/or NoSQL).
• Solid foundation in statistics, including regression, time-series analysis, factor modelling, and signal processing.
• Good understanding of financial market structure, pricing dynamics, and liquidity.
• Ability to work effectively in a remote, distributed research environment.

Responsibilities

• Conduct in-depth quantitative analysis of hedge fund performance, including return decomposition, risk metrics, and factor exposures.
• Develop and maintain proprietary analytical frameworks to assess manager skill, performance persistence, and style consistency across market regimes.
• Perform attribution and factor-based analysis to evaluate alignment between managers’ stated investment processes and realised results.
• Build and maintain factor and risk models to analyse correlations, beta exposures, and portfolio overlap across the manager universe.
• Analyse portfolio-level characteristics such as liquidity profiles, concentration, leverage, and counterparty exposures.
• Provide quantitative support to the CIO for manager selection, due diligence, and ongoing monitoring.
• Produce high-quality analytical materials for the investment committee, translating complex quantitative results into actionable insights.
• Source, clean, and normalise alternative datasets, including sentiment, social media, and ESG data.
• Develop predictive models and signals using time-series analysis, statistical techniques, and machine-learning methods.
• Design and maintain robust backtesting frameworks, incorporating transaction costs and market impact.
• Build and monitor risk models and conduct stress testing across different market scenarios.
• Document research methodologies and clearly present findings to internal stakeholders.

Skills

Masters/PhD

Python (pandas)

Quantitative modeling

Time-series analysis

SQL/NoSQL databases

Statistics

Remote collaboration

Education

Master's or PhD in quantitative field

Tools

SQL/NoSQL databases

Python tooling

Cloud platforms

Job description

We are seeking a Quantitative Analyst to join our research team in afully remote capacity . The team focuses on extracting market-relevant insights from alternative data, sentiment indicators, and rigorous quantitative analysis. This role is research-intensive and hands‑on, involving signal development, model validation, and systematic evaluation of investment ideas across multiple asset classes. The ideal candidate is intellectually curious, technically strong, and motivated to translate complex datasets into robust, decision‑ready insights while working in a distributed environment.

Core Responsibilities

Hedge Fund & Manager Research

1. Conduct in-depth quantitative analysis of hedge fund performance, including return decomposition, risk metrics, and factor exposures
2. Develop and maintain proprietary analytical frameworks to assess manager skill, performance persistence, and style consistency across market regimes
3. Perform attribution and factor-based analysis to evaluate alignment between managers’ stated investment processes and realised results
4. Build and maintain factor and risk models to analyse correlations, beta exposures, and portfolio overlap across the manager universe
5. Analyse portfolio‑level characteristics such as liquidity profiles, concentration, leverage, and counterparty exposures
6. Provide quantitative support to the CIO for manager selection, due diligence, and ongoing monitoring
7. Produce high‑quality analytical materials for the investment committee, translating complex quantitative results into actionable insights

Broader Asset Class & Data Research

1. Source, clean, and normalise alternative datasets, including sentiment, social media, and ESG data
2. Develop predictive models and signals using time‑series analysis, statistical techniques, and machine‑learning methods
3. Design and maintain robust backtesting frameworks, incorporating transaction costs and market impact
4. Build and monitor risk models and conduct stress testing across different market scenarios
5. Document research methodologies and clearly present findings to internal stakeholders

Required Experience & Skills

1. Master’s or PhD in Finance, Economics, Mathematics, Statistics, Computer Science, Engineering, or a related quantitative discipline
2. Experience in quantitative research, data science, or analytics within financial markets (buy‑side or sell‑side)
3. Proven ability to design, implement, and validate quantitative models using real‑world market data
4. Strong proficiency in Python for research and modelling (pandas, numpy, scipy, statsmodels, scikit‑learn)
5. Experience working with large datasets and databases (SQL and/or NoSQL)
6. Solid foundation in statistics, including regression, time‑series analysis, factor modelling, and signal processing
7. Good understanding of financial market structure, pricing dynamics, and liquidity
8. Ability to work effectively in aremote, distributed research environment, managing priorities and collaborating across time zones

Desirable Skills & Experience

1. Experience working with sentiment, news, social media, or other alternative datasets
2. Background in machine learning, NLP, or other advanced modelling techniques applied to financial data
3. Familiarity with cloud‑based data and research environments (AWS, GCP, Azure)
4. Exposure to portfolio construction, risk analytics, or systematic factor‑based strategies