Full-Time

Data Scientist

Trading Operations

Confirmed live in the last 24 hours

The Voleon Group

The Voleon Group

201-500 employees

Investment management using machine learning algorithms

Fintech
Quantitative Finance

Compensation Overview

$150k - $190kAnnually

+ Bonus

Entry, Junior

Berkeley, CA, USA

Hybrid position in Berkeley.

Category
Data Science
Quantitative Analysis
Quantitative Finance
Data & Analytics
Required Skills
Bash
Data Science
R
Git
SQL
Financial analysis
Pandas
Linux/Unix
Data Analysis
Requirements
  • 1+ years of applied end-to-end industry experience, including internships working with complex datasets, including curation, querying, aggregation, exploratory data analysis, and visualization
  • Experience using statistical methods to analyze data, identify patterns, conduct root cause analysis, discover insights, and recommend solutions
  • Ability to frame and answer questions mathematically
  • Ability to infer useful forward-looking directions from results of retrospective analysis
  • Fluency in managing, processing, and visualizing tabular data using a combination of SQL, Pandas, and R
  • Basic software development skills and experience with bash, Linux/Unix, and git
  • Ability to refine requirements from ambiguous requests to produce reports
  • Excellent communication skills
  • Bachelor’s degree in a quantitative discipline (statistics, biostatistics, data science, computer science, or a related field)
Responsibilities
  • Design and implement systems to ensure data correctness and monitor data health in data stores and live feeds
  • Proactively identify abnormal production behavior and communicate them clearly to relevant stakeholders
  • Perform extemporaneous analyses on research and production trading systems with leadership
  • Harness financial expertise and statistical analysis to gain actionable insights into our production trading and research systems
  • Design and implement analysis pipelines that automate those analyses found to be valuable for ongoing monitoring

Voleon focuses on investment management by utilizing machine learning to analyze financial market trends and identify patterns for investment decisions. The firm serves institutional clients, such as pension funds and endowments, and distinguishes itself by relying on data-driven algorithms rather than human intuition. Voleon's revenue model is based on managing client portfolios with performance-based fees, aligning its interests with those of its clients. With a strong emphasis on academic research, Voleon aims to stay at the forefront of financial prediction technology.

Company Stage

N/A

Total Funding

N/A

Headquarters

Berkeley, California

Founded

2007

Growth & Insights
Headcount

6 month growth

3%

1 year growth

3%

2 year growth

3%
Simplify Jobs

Simplify's Take

What believers are saying

  • Working at Voleon offers the opportunity to be at the forefront of integrating machine learning with financial market analysis.
  • The firm's focus on continuous learning and academic research provides a stimulating environment for professional growth.
  • Voleon's performance-based fee structure can lead to significant financial rewards for employees if the firm's investment strategies succeed.

What critics are saying

  • The highly competitive nature of the financial markets sector means that Voleon must continuously innovate to maintain its edge.
  • Reliance on machine learning algorithms carries the risk of model failure or inaccuracies, which could lead to significant financial losses.

What makes The Voleon Group unique

  • Voleon leverages advanced machine learning algorithms to predict financial market trends, setting it apart from traditional investment firms that rely on human intuition.
  • The firm's strong emphasis on academic research and intellectual rigor ensures that it remains at the cutting edge of technological advancements in financial prediction.
  • Voleon's performance-based fee structure aligns its interests with those of its clients, incentivizing optimal investment returns.

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