Job Description

🌟 Internship Overview

Duration: 10 weeks (Summer 2026)
Location: SoHo, New York City (Hybrid)
Focus: Research-driven data science applied to finance, markets, and economic systems.

Two Sigma is one of the most prestigious quant finance + data science employers globally, comparable to top firms like Jane Street, Citadel, and DE Shaw.

🎯 What You’ll Work On

🔬 Research & Modeling

• Develop hypotheses from large real-world datasets
• Apply cutting-edge statistical and ML techniques
• Extract meaningful signals from noisy data

🤝 Collaboration

• Work with engineers and business stakeholders
• Partner with a dedicated mentor
• Present final project to leadership

🎓 Academic Engagement

• Reading groups on latest research papers
• Academic seminars with university experts
• Research-oriented culture (very PhD-like environment)

🧠 Required Qualifications

You’re a strong candidate if you have:

✅ Degree in quantitative field

• Computer Science
• Statistics
• Mathematics
• Economics
• Physics / Engineering

✅ Skills

• Python (very important)
• SQL
• Research project experience
• Statistical thinking
• Ability to communicate complex ideas

❗ Finance background NOT required.

This is important — Two Sigma hires many students without finance knowledge.

💰 Salary (Very High for Internship)

Weekly pay:

• $3,800/week (Bachelor’s)
• $3,900/week (Master’s)
• $4,200/week (PhD)

That equals roughly:

👉 $38k – $42k for 10 weeks

This is among the highest paying internships in the world for data science.

🚀 What Makes This Internship Special

• Real research ownership (not just support work)
• Exposure to quantitative trading methodologies
• Top-tier mentorship
• Extremely strong brand value on resume
• Potential full-time return offer

Having Two Sigma on your resume can open doors to:

• Quant Research roles
• Data Scientist roles at FAANG
• AI research positions
• Hedge funds / fintech firms

⭐ How Competitive It Is

Very competitive (top 1–3%).

Typical selected candidates have:

✔ Strong math/stats foundation
✔ Research experience or publications
✔ Advanced ML projects
✔ Competitive programming OR Kaggle
✔ Top university or exceptional projects

PhD students often apply, but undergraduates also get selected.

🔥 How to Improve Your Chances

1. Build research-level projects, not basic tutorials.
2. Learn:
   • Probability & statistics deeply
   • Linear algebra
   • Optimization
3. Do one serious project like:
   • Time series forecasting
   • Market prediction
   • Causal inference
   • Reinforcement learning
4. Participate in Kaggle or research competitions.
5. Practice coding interviews (LeetCode medium level).