Application Guide

How to Apply for Data Engineer

at Forecasting Research Institute

🏢 About Forecasting Research Institute

The Forecasting Research Institute is unique in its focus on developing forecasting methods to improve decision-making, particularly in high-stakes domains like AI safety and global catastrophic risks. As a remote-first organization, they offer the opportunity to work on cutting-edge research with direct real-world impact, collaborating with experts in forecasting and decision science.

About This Role

As the first dedicated Data Engineer at FRI, you'll be building the foundational data infrastructure from the ground up, making key architectural decisions that will shape how forecasting data is collected, processed, and analyzed across dozens of research projects. This role is impactful because you'll enable researchers to work with reliable, standardized data to develop better forecasting methods that could improve critical decision-making in areas like AI governance and risk assessment.

💡 A Day in the Life

A typical day might involve designing dimensional models to standardize new forecasting data from research projects, implementing data quality checks on incoming survey responses, and setting up monitoring alerts for production data pipelines. You'd collaborate with researchers to understand their data needs while making architectural decisions about FRI's evolving data infrastructure.

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Interview Prep

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🎯 Who Forecasting Research Institute Is Looking For

  • Has experience designing and implementing data infrastructure from scratch, not just maintaining existing systems
  • Can demonstrate practical knowledge of dimensional data modeling specifically for research or survey data
  • Has built production data pipelines that handle diverse data sources (surveys, expert panels, AI systems)
  • Understands how to implement data quality controls and access management in a research environment where data sensitivity varies

📝 Tips for Applying to Forecasting Research Institute

1

Highlight specific experience with survey data or research data pipelines, as FRI collects forecasting data from surveys and expert panels

2

Demonstrate your architectural decision-making process with examples of trade-offs you've made in previous data infrastructure projects

3

Show how you've implemented data quality controls in past roles, particularly for data used in research or analysis

4

Mention any experience with forecasting, prediction markets, or decision science data if applicable

5

Explain how you approach documentation and knowledge sharing in a technical team, as you'll be setting standards as the first data engineer

✉️ What to Emphasize in Your Cover Letter

['Your experience making architectural decisions for data infrastructure, especially in early-stage or research environments', "Specific examples of building data pipelines for diverse data sources similar to FRI's surveys, expert panels, and AI systems", "How you've implemented data quality management and access controls in previous roles", "Why you're interested in forecasting research specifically and how your skills align with FRI's mission"]

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🔍 Research Before Applying

To stand out, make sure you've researched:

  • → Read FRI's published research papers on forecasting methods to understand their technical approach
  • → Explore their website's research projects to understand the types of forecasting data they work with
  • → Look into their team members' backgrounds to understand the research culture you'd be supporting
  • → Review any public talks or presentations by FRI staff about their data needs and challenges
Visit Forecasting Research Institute's Website →

💬 Prepare for These Interview Topics

Based on this role, you may be asked about:

1 How would you design a data pipeline architecture for ingesting forecasting data from surveys, expert panels, and AI systems simultaneously?
2 What dimensional modeling approach would you use to standardize data across dozens of different research projects?
3 How would you implement data quality checks for forecasting data where accuracy and reliability are critical for research validity?
4 What orchestration tools and monitoring strategies would you recommend for production data pipelines in a research organization?
5 How would you balance data accessibility for researchers with appropriate access controls for sensitive forecasting data?
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⚠️ Common Mistakes to Avoid

  • Focusing only on large-scale corporate data engineering without showing experience in research or early-stage environments
  • Presenting generic data pipeline examples without tailoring them to FRI's specific forecasting data sources
  • Neglecting to discuss data quality and documentation, which are explicitly highlighted in the requirements

📅 Application Timeline

This position is open until filled. However, we recommend applying as soon as possible as roles at mission-driven organizations tend to fill quickly.

Typical hiring timeline:

1

Application Review

1-2 weeks

2

Initial Screening

Phone call or written assessment

3

Interviews

1-2 rounds, usually virtual

✓

Offer

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Good luck with your application to Forecasting Research Institute!

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