Remote Economic Analyst – Financial Markets for AI Training

Alignerr Corp.; Seattle (WA); Remote; USD 55,000 - 110,000; Part time

Benefits offered by this job

Remote work

Flexible hours

Potential for ongoing engagements

Job summary

Alignerr is seeking an Economic Analyst to review macro data, interpret regulatory commentary, and produce structured, high-precision analysis for AI training in financial markets.

This fully remote hourly contract offers flexible hours, independent work, and opportunities for ongoing engagements as new projects launch. You will analyze datasets, compare narratives to indicators, and summarize trends and risks for internal models.

Qualifications

• Background in economics, finance, financial markets, or policy research.
• Ability to translate macro data into clear written analysis.
• Strong analytical writing with precision and clarity.
• Comfortable working independently within structured, task-based workflows.

Responsibilities

• Analyze macroeconomic datasets, market movements, and underlying drivers.
• Compare narrative reports and qualitative commentary against quantitative indicators.
• Evaluate impact of monetary policy, regulatory actions on markets.
• Produce clear, structured summaries of trends, risks, and scenario considerations.
• Support internal modeling and research workflows with accurate analysis.
• Maintain consistency and rigor across recurring analytical tasks.

Skills

Macroeconomics

Financial markets

Quantitative analysis

Written communication

Independent work

Education

Economics/Finance degree

Tools

Python

R

Excel

Job description

Alignerr is seeking an Economic Analyst to review macro data, interpret regulatory commentary, and produce structured, high-precision analysis for AI training in financial markets.

This fully remote hourly contract offers flexible hours, independent work, and opportunities for ongoing engagements as new projects launch. You will analyze datasets, compare narratives to indicators, and summarize trends and risks for internal models.