Quantitative Data Scientist

Kpler; New York (NY); On-site; USD 80,000 - 110,000; Full time

Job summary

Kpler, located in New York, NY, is seeking a Quantitative Data Scientist. This role will be essential in merging research with technology, aiding systematic trading and options research through robust data handling and analytics.

The successful candidate will develop and maintain data pipelines, conduct thorough data analyses, and support quantitative researchers with scalable tools. A strong mathematical and coding background is essential for success in managing large financial datasets.

Qualifications

• 1-3 years of experience in data engineering, data science, or quantitative technical roles.
• Strong ability to write clean and efficient Python code.
• Experience in building automated data pipelines.
• Solid understanding of probability and statistics.

Responsibilities

• Partner with researchers to acquire datasets for trading.
• Design and maintain Python-based data pipelines.
• Develop workflows for systematic trading research.
• Perform exploratory data analysis for hypothesis testing.

Skills

Python programming skills

Data engineering

Statistical analysis

Data pipeline development

Mathematical foundations

Job description

The Quantitative Data Scientist will serve as a critical bridge between research and technology, enabling the firm's systematic trading and options research initiatives through robust data acquisition, engineering, and analytics capabilities.

This role is responsible for sourcing and integrating new datasets, building and maintaining reliable research data pipelines, developing analytical processes to transform raw data into actionable insights, and supporting quantitative researchers with scalable tools and infrastructure. The ideal candidate combines strong software engineering skills with a solid mathematical and statistical foundation and is comfortable working hands‑on with large financial datasets.

Responsibilities

• Partner closely with quantitative researchers to identify, evaluate, and acquire new datasets relevant to trading and market research initiatives.
• Design, build, and maintain reliable Python-based data pipelines for collecting, cleaning, transforming, and storing research data.
• Develop automated workflows and processes to support systematic trading research and strategy development.
• Create analytical frameworks and tooling to process large datasets and generate statistical insights.
• Build and maintain research databases, data models, and data quality monitoring processes.
• Perform exploratory data analysis and statistical investigations to support alpha generation and hypothesis testing.
• Collaborate with researchers to operationalize research methodologies into repeatable analytical workflows.
• Manage data infrastructure running on cloud or dedicated server environments, ensuring stability, reliability, and performance.
• Document data sources, pipeline architecture, methodologies, and analytical processes to support knowledge sharing and reproducibility.
• Stay current on emerging data sources, technologies, and quantitative research techniques relevant to financial markets and options trading.

Qualifications

• Circa 1‑3 years of experience in data engineering, data science, quantitative technical role.
• Strong Python programming skills with the ability to write clean, maintainable, and efficient code.
• Experience building and maintaining automated data pipelines.
• Strong understanding of probability, statistics, and quantitative analysis.
• Solid mathematical foundation, including multivariable calculus, linear algebra, and statistical inference.
• Experience working with large datasets and relational databases.
• Demonstrated ability to translate research requirements into technical solutions.
• Experience working in Linux/server–based environments.

Desired Qualifications

• Experience in financial markets, trading, or quantitative investing.
• Familiarity with options markets, derivatives, and volatility products.
• Experience supporting systematic trading or quantitative research teams.
• Knowledge of cloud infrastructure (AWS, GCP, Azure).
• Experience with time‑series analysis and financial data.
• Exposure to machine learning techniques and predictive modeling.
• Experience working with alternative data sources.