L/S Equity - Sector Data Science

Verition Group LLC; New York (NY); On-site; USD 120,000 - 180,000; Full time

Job summary

Verition Fund Management LLC is seeking a Data Scientist to join the centralized Long/Short Equity team. This role focuses on sourcing, analyzing, and operationalizing datasets to enhance investment decisions and alpha generation.

You will collaborate with portfolio managers and analysts, apply statistical methods and ML, and develop scalable analytics frameworks for discretionary equity investing.

Qualifications

• 2+ years of data science or quantitative research experience in finance or related fields.
• Proficiency in Python and data libraries (pandas, NumPy).
• Experience with SQL and large datasets in modern data processing pipelines.
• Understanding of equity markets and investment workflows is a plus.
• Strong problem-solving and communication skills for cross-functional stakeholders.

Responsibilities

• Analyze large structured and unstructured datasets to identify predictive signals for L/S Equity.Source, evaluate, and onboard alternative datasets relevant to equity investing.
• Collaborate with PMs and analysts to understand investment processes and build data-driven tools.
• Apply statistical methods and ML techniques to improve signal generation and portfolio insights.
• Build dashboards and reporting tools for fast, data-informed decision making.

Skills

Python

SQL

Data analysis

Machine learning

Communication

Generative AI tooling

Tools

pandas

NumPy

scikit-learn

Job description

Verition Fund Management LLC (“Verition”) is a multi-strategy, multi-manager hedge fund founded in 2008. Verition focuses on global investment strategies including Global Credit, Global Convertible, Volatility & Capital Structure Arbitrage, Event-Driven Investing, Equity Long/Short & Capital Markets Trading, and Global Quantitative Trading.

We are seeking a highly motivated and technically strong Data Scientist to join the centralized Long/Short Equity team at a leading multi-strategy hedge fund. This team partners directly with portfolio managers and analysts across the L/S Equity business to identify, evaluate, and deploy data-driven insights that enhance investment decision-making and alpha generation.

The ideal candidate will combine strong technical and analytical capabilities with a practical understanding of financial markets. This role will focus on sourcing, analyzing, and operationalizing alternative and traditional datasets, building research tools, and developing scalable analytical frameworks that support discretionary equity investing.

Key Responsibilities:

• Analyze large structured and unstructured datasets to identify predictive signals and investment insights for L/S Equity portfolio managers.
• Source, evaluate, and onboard alternative datasets relevant to equity investing, including consumer, transactional, web, geolocation, sentiment, and fundamental datasets.
• Work closely with portfolio managers, analysts, and sector teams to understand investment processes and develop tailored data-driven solutions.
• Apply statistical techniques and machine learning methods where appropriate to improve signal generation, company analysis, and portfolio insights.
• Create dashboards, visualizations, and reporting tools that enable PMs and analysts to consume data effectively and make faster investment decisions.

Required Qualifications:

• 2+ years of experience in data science, quantitative research, or data analytics within a hedge fund, asset manager, investment bank, or technology-focused environment.
• Strong proficiency in Python, including experience with libraries such as pandas, NumPy, scikit-learn, and related data science tools.
• Experience working with large datasets, SQL databases, APIs, and modern data processing frameworks.
• Understanding of equity markets and investment workflows, ideally within a long/short equity investing environment a plus but not required
• Strong problem-solving and critical thinking abilities with a demonstrated ability to derive actionable insights from complex datasets.
• Ability to communicate findings clearly to both technical and non-technical stakeholders, including portfolio managers and investment analysts.
• Exposure to generative AI tooling for investment research workflows.
• Knowledge of software engineering best practices, including version control and CI/CD workflows.