Data Science & AI Engagement, Healthcare Data Scientist
Role summary
This Data Scientist role operates at the intersection of analytics, generative AI, and financial markets within the healthcare industry. The position involves leveraging alternative and financial data, along with AI tools, to generate actionable investment insights. Key responsibilities include engaging with investment teams, analyzing data to uncover signals, and building AI-powered solutions to enhance research and decision-making. The role requires expertise in Python and SQL, a solid understanding of time-series and financial data, and a client-facing mindset to support business stakeholders and drive AI adoption.
Role Overview
As part of the Data Science & AI organization, we operate at the intersection of cutting-edge analytics, generative AI, and dynamic financial markets. We seek a highly motivated Data Scientist with a passion for leveraging alternative data, financial data, and AI-powered tools to generate actionable insights for our investment business. In this role, you'll partner with Portfolio Managers and Analysts to build advanced data solutions, drive adoption of our generative AI products, inform key investment decisions, and directly impact the performance of the firm.
Embedded within a global team of data scientists, AI specialists, and content experts, you will collaborate closely with colleagues 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 and AI needs and proactively offering solutions that give them a competitive advantage in their investment process. For your coverage, you are the single point of contact for all inbound data science and AI workflows
Key Responsibilities
- Investment Team Engagement & Support:
Proactively train and support users on data and AI tools, identify opportunities to apply LLMs and data science techniques to streamline workflows, and translate their needs into actionable projects to drive research efficiency and idea generation.
- Data Analysis & Modeling:
Source, wrangle, and analyze alternative datasets to uncover investment signals, partnering with the alternative data research team on predictive modeling using a variety of structured and unstructured datasets.
- Product & Process Improvement:
Partner with investment teams to build and maintain data and AI products that improve their workflows, clearly communicate analytical findings, and pilot emerging data analysis techniques.
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.
- Experience in a client-facing role, ideally in the healthcare sectors, scoping and delivering technical solutions to business stakeholders, is highly valued.
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.
- Demonstrated understanding of how investment professionals use (and want to use) AI in their workflows, and of the common challenges in driving AI adoption within this user base
- Familiarity with AWS or other cloud platforms, Git for version control, and Airflow (or similar orchestration tools) is a 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.
- Comfortable teaching and presenting to non-technical audiences.
- 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 genuinely curious about new technologies.