Data Scientist - Equities

As part of the Data Science & AI organization, we operate at the intersection of cutting-edge analytics and dynamic financial markets. We seek a highly motivated Data Scientist with a passion for leveraging alternative and financial data to generate actionable insights for our investment business. In this role, you’ll partner with Portfolio Managers and Analysts to build advanced data solutions, inform key investment decisions, and directly impact the performance of the firm.
Embedded within a global team of data analysts, data scientists, and content experts, you will collaborate closely with colleagues across multiple geographies to deliver high-impact results. In addition to hands-on data science work, you will serve as a trusted liaison to various investment professionals-anticipating their data needs and proactively offering solutions that will give them a competitive advantage in their investment process.
Key Responsibilities
Investment Team Engagement & Support

• Act as investment teams’ go-to person for a suite of data products, with a particular focus on alternative (non-traditional) and financial datasets.
• Proactively train, guide, and support investment teams in the effective use of data-driven products, ensuring high adoption and satisfaction.
• Gather stakeholder feedback and consistently refine internal knowledge bases, documentation, and training resources to address evolving needs.

Data Analysis & Modeling

• Source, wrangle, and analyze large datasets (including alternative datasets such as satellite imagery, web-scraped data, credit card insights, etc.) to uncover investment signals.
• Develop predictive models that incorporate both structured and unstructured data (e.g., time-series, text-based, event-based) to drive research insights.
• Collaborate with cross-functional teams to automate data pipelines using Python, SQL, and relevant workflow tools (e.g., Airflow, Git, AWS).

Product & Process Improvement

• Partner with internal stakeholders (Investment Teams, Engineering, Product) to ideate, implement, and maintain robust data products that enhance portfolio performance.
• Translate complex analytical findings into clear recommendations for technical and non-technical audiences.
• Identify innovative techniques and emerging technologies to keep the team at the forefront of analytics (Generative AI, machine learning, NLP, cloud computing, etc.).

Collaboration & Continuous Growth

• Collaborate with a global network of data professionals to share best practices and ensure consistency in data science approaches worldwide.
• Drive continuous improvement initiatives by proactively suggesting new features and enhancements to existing systems based on user feedback and emerging trends.

Qualifications & Requirements

• Education & Experience
  • 3-6 years of professional experience in data science, analytics, or a closely related field.
  • Bachelor’s/Master’s degree in Mathematics, Engineering, Economics, Computer Science, or a related discipline.
• Technical Proficiency
  • Expertise in Python for data manipulation and modeling, along with strong SQL skills for database querying.
  • Solid understanding of time-series data, financial data structures, and the nuances of alternative datasets.
  • Hands-on exposure to Large Language Models (LLMs) is a strong plus.
  • Familiarity with AWS or other cloud platforms, Git for version control, and Airflow (or similar orchestration tools) is a strong plus.
• Client-Centric Mindset
  • Experience supporting or partnering with investment professionals in a high-stakes environment.
  • Outstanding communication skills-capable of bridging the gap between technical depth and business relevance with urgency.
  • Excellent organizational habits, follow-through, and stakeholder management.
• Additional Attributes
  • Strong attention to detail with a focus on data accuracy and consistency.
  • Creative problem solver with a track record of troubleshooting challenging data issues.
  • Self-motivated, eager to learn, and open to new technologies and methodologies.

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