Head of Feature Engineering at The Voleon Group

Berkeley, California, United States

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Not SpecifiedCompensation

Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

Finance, Investment ManagementIndustries

Requirements

Demonstrable clarity of thought
Grit, in large quantities
7+ years of experience in quantitative finance, signal research, or predictive modeling, including at least 2 years leading research teams or initiatives
Strong scientific programming skills in Python and/or R; experience with SQL or Spark is a plus
Deep understanding of financial markets, company fundamentals, econometrics, and financial-economic models
Extensive experience designing features for use in machine-learning pipelines or research environments (e.g., PyTorch, scikit-learn)
Master's degree in a relevant quantitative field
Preferred: Ph.D. in a quantitative field
Preferred: Experience managing globally distributed research or data-analysis teams
Preferred: Familiarity with alternative data

Responsibilities

Lead the design and development of features for use in supervised and unsupervised learning, drawing on a variety of econometric and financial concepts
Translate economic and financial model constructs into scalable, empirical signal definitions, using structured and unstructured data
Collaborate with data engineering teams to implement clean, robust pipelines for feature generation and version control across large securities universes
Shape the experimental framework for evaluating feature performance in predictive models
Establish and manage best practices for documenting construction logic, assumptions, and behavior of each feature under different economic and market conditions
Manage and coach the feature engineering team, fostering a collaborative and results-driven environment
Align the team’s efforts with broader research and model development goals through strategic planning and cross-functional collaboration

Skills

Machine Learning

Feature Engineering

Econometrics

Supervised Learning

Unsupervised Learning

Data Pipelines

Version Control

Financial Modeling

Statistical Methods

The Voleon Group

Investment management using machine learning algorithms

About The Voleon Group

Voleon focuses on investment management by utilizing machine learning to analyze financial market trends. The firm uses advanced statistical models to process large datasets and identify patterns that inform investment decisions, setting it apart from traditional methods that rely on human intuition. Voleon serves institutional clients and operates on a performance-based fee structure, aligning its interests with those of its clients. The company's goal is to provide data-driven insights that optimize investment returns while adapting to changing market conditions.

Berkeley, CaliforniaHeadquarters

2007Year Founded

VENTURE_UNKNOWNCompany Stage

Quantitative Finance, Financial ServicesIndustries

51-200Employees

Benefits

Health Insurance

Dental Insurance

Vision Insurance

Life Insurance

Paid Vacation

Paid Sick Leave

401(k) Retirement Plan

401(k) Company Match

Risks

Competition from other quantitative hedge funds may erode Voleon's market share.

Regulatory scrutiny on AI use in finance could increase compliance costs for Voleon.

Data quality issues could lead to inaccurate predictions and financial losses for Voleon.

Differentiation

Voleon uses machine learning for data-driven financial market predictions.

The firm serves institutional clients with a focus on scalability and risk management.

Voleon's academic approach emphasizes intellectual rigor and continuous learning.

Upsides

Increased interest in ESG investing offers new opportunities for Voleon's models.

Alternative data sources enhance predictive models for quantitative hedge funds like Voleon.

Cloud computing enables efficient scaling of Voleon's data processing capabilities.

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