ML Data Engineer/Scientist
@ Hunter Bond

Hybrid
$250,000
Hybrid
Full Time
Posted 1 day ago

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XXXXXXXXX XXXXXXXXX XXXXXXXX****** @hunterbond.com
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Job Details

About the Role

Hunter Bond is building a new team at a stealth quant fund focused on alternative data. As an ML Data Engineer/Scientist, you will research, develop, and test statistical models to identify alpha-generating opportunities in large, complex datasets.

Key Responsibilities

  • Conduct data science research using statistical, econometric, and mathematical techniques.
  • Analyze vast financial and alternative datasets for insights.
  • Back test strategies and validate models rigorously.
  • Collaborate to deploy models into production systems.
  • Stay current with academic and industry research trends.

Opportunity & Compensation

This early core team member role offers up to $250k salary with performance-based bonuses, and an opportunity to partner plus equity in the fund within 12 months. With planned fund growth from >$300M to a potential $1B AUM, you will work with a team of highly educated professionals from MIT, Harvard, Cambridge, and Oxford.

Key skills/competency

  • Data Science
  • Statistical Analysis
  • Quantitative Research
  • Python
  • C++
  • SQL
  • AWS
  • Financial Markets
  • Algorithm Development
  • Time-series Analysis
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How to Get Hired at Hunter Bond

🎯 Tips for Getting Hired

  • Customize your resume: Highlight data science and quant research skills.
  • Showcase technical expertise: Emphasize Python, C++, SQL, and AWS.
  • Detail quant experiences: Describe relevant financial market projects.
  • Prepare for interviews: Review statistical and time-series problem solving.

📝 Interview Preparation Advice

Technical Preparation

Review Python and C++ coding challenges.
Practice SQL queries and AWS tasks.
Study statistical and time-series analysis methods.
Simulate back testing of trading strategies.

Behavioral Questions

Describe a challenging quantitative project.
Explain teamwork in high-pressure environments.
Discuss problem-solving under tight deadlines.
Share experience handling large datasets.

Frequently Asked Questions