data analyst
Bella Private Markets Overview
Bella Private Markets is a consulting and advisory firm focused exclusively on providing solutions to the challenges facing the private capital industry. Led by Dr. Josh Lerner of Harvard Business School, Bella works closely with the senior management of – among others – private equity groups, venture capital firms, and institutional investors on complex, customized projects. Our approach combines a rigorous academic perspective with real world industry expertise to provide our clients with actionable insights to improve performance and optimize operations.
Data Analyst Overview
Data Analysts at Bella engage in a broad range of research related to private equity, venture capital, and innovation. Data Analysts work closely with Bella colleagues to assist them with investment performance benchmarking and analytics, economic research, and government policy reports requiring empirical evaluation.
Bella offers Data Analysts the opportunity to explore in-depth the notoriously opaque private equity industry. Beyond working with Dr. Josh Lerner, named one of the most influential people in private equity, Data Analysts may also work with private equity investors themselves.
Essential Duties and Responsibilities
Quantitative research and analysis
Data Analysts should be comfortable with data. Data Analysts navigate academic journals and private equity databases to find specific articles and data of interest. They are also involved in quantitative modeling projects. Data Analysts should have experience using R or Python at a minimum and be able to demonstrate an ability to process data, execute analyses, and derive clear and presentable insights from data.
General research
Data Analysts synthesize large amounts of information to offer a thorough overview of a range of topics. The value-add on many projects stems from the use of a variety of sources to present a clear picture—from general context to fine detail—on a given issue. Bella launches Data Analysts into the world of private equity and will often ask Data Analysts to work on projects on which they have minimal prior knowledge.
Project scoping
Bella projects are often open-ended in nature and are completed without specific guidelines from the client. Data Analysts should be prepared to help scope projects, develop methodologies, and plan analyses.
Internal support
Data Analysts may be tasked with completing internal projects related to Bella’s business development and marketing needs as directed by Bella management.
Data Analysts will perform other related duties as assigned by management.
Skills and Qualifications
Accountability
Completes high-quality work within a timely manner
Manages time efficiently
Adaptability
Willing to contribute across projects as needed
Communication
Writes and presents ideas effectively and concisely
Prepares written methodologies describing analytical projects
Proficient in English
Continual learning
Understands knowledge limitations
Asks questions to learn in the discipline
Initiative
Suggests improvements as appropriate
Integrity
Is thorough and detail-oriented in all work
Professionalism
Maintains appropriate and respectful demeanor in all situations
Systems thinking
Maintains big-picture understanding of a project and understands how details relate to overall project goals
Teamwork
Comfortable engaging with colleagues to ask questions and offer ideas
Education and Experience Qualifications
Basic qualifications for the Data Analyst position include:
An undergraduate degree in Statistics, Applied Mathematics, or similarly quantitative fields with a minimum GPA of 3.5.
Two years of related work experience.
A Master’s degree in Statistics, Applied Mathematics, or similar can substitute for the required work experience.
Internships, research roles, or similar non-full-time experiences involving data science projects will be considered toward this requirement.
Proficiency with R or Python.
Location
Boston, MA (hybrid work schedule)
Application
Applicants should submit the following to hiring@bella-pm.com:
Cover letter.
Resume.
Academic transcript (undergraduate and graduate, if applicable).
Coding sample (ideally, showing a basic analysis in R or Python).