The Voleon Group

Data Scientist - Feature Engineering

The Voleon Group$120K — $150K *
Finance & Insurance
Less than 5 years of experience
Job Overview by Ladders

Qualifications

  • 2 years of experience with complex datasets including curation and exploratory analysis
  • Proficiency in SQL and Python for data management and visualization
  • Strong statistical analysis skills for identifying patterns
  • Ability to translate data insights into actionable strategies
  • Bachelor's degree in a quantitative field like data science or statistics
  • Basic software development knowledge, including bash and git
  • Strong communication skills for presenting findings clearly

Responsibilities

  • Explore and curate messy datasets to understand their capabilities
  • Design predictive features using financial intuition and statistical methods
  • Validate features through disciplined, test-driven frameworks
  • Build and maintain data pipelines, monitoring data health
  • Communicate data findings and stories clearly to team and leadership
  • Investigate data anomalies and conduct root cause analysis
  • Leverage AI tools to enhance coding and data exploration

Benefits

  • Opportunities to work with advanced AI tools
  • Involvement in high-impact projects directly influencing investment processes
  • Collaborative environment focused on curiosity and innovation
  • Access to a well-resourced team working with diverse datasets
  • Potential for substantial referral bonuses for candidate recommendations
Full Job Description
The Voleon Group is growing its Feature Engineering team - a small, high-impact group responsible for turning the world's messy, complex datasets into predictive signals that power our machine learning models. As a Data Scientist on this team, you'll dig into raw data from diverse domains and, in collaboration with Research, assist in design and implementation of features that capture what is actually happening in the world. This is data storytelling at its most consequential: every feature you build has a direct path to our investment process.

We're looking for deeply curious people who get genuine satisfaction from wrestling with an unfamiliar dataset, understanding its structure and quirks, and emerging with something that encodes real information. You'll own the full arc from data sourcing and curation through feature construction, statistical validation, and integration into our production systems. We also expect you to actively experiment with AI-powered tools - LLM-based coding assistants, agentic workflows, and whatever comes next - to accelerate your day-to-day work and push the boundaries of what a small team can accomplish.

Responsibilities
  • Explore, profile, and curate complex and often messy datasets from third-party vendors and internal sources, developing a deep understanding of what each dataset can and cannot tell us
  • Harness financial intuition, academic research, and statistical rigor to inform design and implementation of predictive features in collaborative setting
  • Validate features through a disciplined, test-driven framework - including cross-sectional analysis, stationarity testing, and point-in-time correctness - to ensure signals are real and not artifacts of data issues
  • Build and maintain data pipelines that bring features from prototype to production, with monitoring for data health and correctness along the way
  • Communicate your findings clearly - both the signal you've found and the story of how the data produces it - to researchers and leadership
  • Proactively investigate anomalies in data feeds and production behavior, performing root-cause analysis and surfacing issues to relevant stakeholders
  • Leverage AI tools to accelerate exploration, coding, and analysis - and share what you learn about effective workflows with the team


Requirements
  • 2 years of applied industry experience (including internships) working end-to-end with complex datasets: curation, querying, aggregation, exploratory analysis, and visualization
  • Experience using statistical methods to analyze data, identify patterns, conduct root-cause analysis, and translate findings into actionable insights
  • Ability to frame and answer questions mathematically
  • Ability to infer useful forward-looking directions from the results of retrospective analysis
  • Fluency in managing, processing, and visualizing tabular data using SQL and Python (Pandas or Polars)
  • Basic software development skills and experience with bash, Linux/Unix, and git
  • Ability to refine ambiguous requests into well-scoped analyses and communicate results with clarity and precision
  • Bachelor's degree in a quantitative discipline (statistics, data science, computer science, economics, physics, or a related field)


Preferred Qualifications
  • Master's degree in a quantitative discipline
  • Prior industry experience or demonstrated interest in finance - academic projects, coursework in financial engineering, or industry internships
  • Familiarity with financial datasets such as Compustat, IBES, or similar vendor data
  • Experience developing in a production-facing environment with standard tooling (CI/CD, git, workflow orchestration)
  • Hands-on experience with AI coding assistants or LLM-based tools in a data science or engineering workflow
  • A track record of curiosity-driven exploration - side projects, Kaggle competitions, research papers, or anything that shows you can't leave an interesting dataset alone


"Friends of Voleon" Candidate Referral Program

If you have a great candidate in mind for this role and would like to have the potential to earn $7,500 - $15,000 if your referred candidate is successfully hired and employed by The Voleon Group, please use this form to submit your referral. For more details regarding eligibility, terms and conditions please make sure to review the Voleon Referral Bonus Program.

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About The Voleon Group

The Voleon Group is a quantitative investment management firm that uses advanced mathematical and statistical techniques to identify and exploit market inefficiencies. The company was founded in 2007 by Michael Kharitonov and Jon McAuliffe and is based in San Francisco, California. Voleon's investment strategies are based on machine learning and artificial intelligence, and the company has a team of over 200 researchers and engineers working to develop and improve its algorithms. Voleon manages several funds, including a long/short equity fund and a futures fund, and has a strong track record of performance. The company is known for its rigorous approach to research and its commitment to transparency and ethical behavior.
Learn more about The Voleon Group
Size
200 employees
Industry
Founded
2007

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