Quantitative Research and Strategy, Senior Associate

RA Capital Management, LLC•Boston, MA

4h•Hybrid

About The Position

The Senior Associate, Quantitative Research & Strategy supports the invest team by leading deep analyses using various types of structured and unstructured data. This role combines strong technical acumen with strategic thinking to generate insights that inform investment theses, market assessments, and commercial strategy. This includes building scalable analytics pipelines, developing automated tools, and delivering compelling data-driven narratives.

Requirements

5 - 8 years of experience working with big data to deliver high-quality, actionable insights for non-technical audiences in a fast-paced, analytically rigorous environment.
Demonstrated ability to translate complex, ambiguous business questions into structured analyses that drive strategic action.
Ability to interpret data trends and present insights in an accessible manner through written reports and basic visualizations
Strong communication skills, able to collaborate across functions and convey ideas concisely.
Capable of managing multiple tasks and priorities with attention to deadlines and quality.
Must be curious, flexible, and able to adapt quickly to changing projects and priorities.
Must be authorized to work in the United States without sponsorship.
Must be based in the Boston area and able to work on a hybrid schedule in our downtown Boston office (consideration will be given to applicants who are willing and able to relocate to MA for the role but relocation assistance is not provided).
Strong proficiency in Python and SQL (5+ yrs of experience), with a preference for direct experience working in PySpark for large-scale data processing.
Proven track record of executing across the data lifecycle – engineering, analytics, and modeling – on multiple, concurrent code-heavy projects.
Proficient in leveraging distributed computing frameworks and processing strategies to analyze large-scale datasets (1+ Tbs) efficiently and at scale.

Nice To Haves

Preference for candidates with experience developing, deploying, and monitoring machine learning models in a production environment to support scalable, data-driven analysis.

Responsibilities

Lead high-impact analyses to uncover insights and patterns in data.
Serve as a strategic thought partner to the investment team, translating complex data into clear insights that shape investment decisions.
Design and maintain scalable data pipelines in Databricks using PySpark for multi-source data integration, transformation, and validation at scale.
Develop analytical frameworks and dashboards that enable real-time monitoring of commercial activity, scenario modeling, and automated performance tracking.
Champion data quality by building validation tools, detecting anomalies, and coordinating issue resolution with external vendors and internal engineering partners.
Apply data science methods – such as clustering, time series analysis, and predictive modeling – to uncover hidden patterns in data.
Present findings clearly and persuasively to internal stakeholders through written narratives, presentations, and visualizations that emphasize actionable takeaways.
Collaborate cross-functionally to test hypotheses, refine priorities, and align insights with broader business objectives.