Data Scientist

Bowden Brown; New York (NY); On-site; USD 140,000 - 190,000; Full time

Job summary

Bowden Brown is seeking a Data Scientist to join a global, systematic investment firm’s quantitative research platform. You will work with large and novel datasets to uncover insights for investment strategies and build predictive features.

The role emphasizes strong Python and SQL proficiency and a rigorous quantitative foundation. Prior financial markets experience is beneficial but not essential; curiosity and attention to detail are valued.

Qualifications

• Strong Python and SQL skills.
• Quantitative background in Statistics, Computer Science, Mathematics, Financial Engineering or related discipline.

Responsibilities

• Explore and evaluate new data sources to support systematic investment strategies.
• Reshape and enhance datasets and create features for predictive modelling.
• Conduct preliminary analyses and share results with quantitative researchers and investment teams.
• Collaborate with Portfolio Managers and researchers to understand data requirements.

Skills

Python

SQL

Data science

Education

Quantitative background

Tools

Python

SQL

Job description

I’m currently partnering with a leading global systematic investment firm looking to hire a Data Scientist into a team at the heart of its quantitative research platform.

This is a data science-focused role working with large, complex and often novel datasets to uncover insights that can support systematic investment strategies. You’ll explore and evaluate new data sources, reshape and enhance datasets, create features for predictive modelling, and conduct preliminary analysis that is shared directly with quantitative researchers and investment teams.

You’ll work closely with Portfolio Managers and researchers to understand their data requirements, investigate the characteristics and potential value of different datasets, and help turn raw information into research-ready data.

They’re looking for candidates with strong Python and SQL skills alongside a rigorous quantitative background in areas such as Statistics, Computer Science, Mathematics, Financial Engineering or a related discipline.

Prior financial markets experience is beneficial but not essential; more important is a genuine interest in working with data, strong analytical instincts and the ability to approach unfamiliar datasets with curiosity and attention to detail.