Tracing our roots to 1928, Wellington Management is one of the world’s largest independent investment management firms. With US $1 trillion in assets under management as of 31 March 2019, we serve as a trusted adviser to institutional clients and mutual fund sponsors in over 55 countries. Our innovative investment solutions are built on the strength of proprietary, independent research and span nearly all segments of the global capital markets, including equity, fixed income, multi-asset, and alternative strategies. As a private partnership whose sole business is investment management, our long-term views and interests are aligned with those of our clients. We are committed to attracting a talented and diverse workforce, and to fostering an open, collaborative culture of inclusivity because we believe multiple perspectives lead to more informed investment and business decisions. As an Equal Opportunity Employer, we welcome people with diverse life experiences, fresh ideas, and specialized subject-matter expertise.

The Team

Wellington Management Company’s Investment Data Science team is a growing, Boston-based team that has the vision and potential to impact investment outcomes throughout the firm. We partner directly with Wellington’s world-class community of investors, applying data science and machine learning to tackle a wide range of questions about companies, industries, and the global economy. The environment is fast-paced and collaborative, and we have deep access to data from both public and proprietary sources. Since data science is a mash-up of multiple research disciplines, we have built a team that features diverse backgrounds and complementary skills. We draw on a range of modeling techniques like Bayesian statistics, machine learning, and natural language processing, and work with large structured and unstructured sources of data to produce investment insights. Our team delivers these insights in the form of dashboards or visualizations, as predictive models, and, in some cases, as signals that can be plugged directly into systematic investment strategies.

The Position

The Investment Data Science team is hiring a senior researcher with expertise in machine learning and statistics. This position will work on a mix of long-term and short-term modeling projects and will advise machine learning practitioners on investment teams throughout the firm.


  • Lead research projects from idea generation and prototyping through validation within the Wellington investment community and deployment into production
  • Develop predictive models to forecast economic and business outcomes
  • Architect ML-based optimizations to existing systems and processes within our data science stack
  • Advise quantitative investment teams who are incorporating machine learning techniques into their research process
  • Mentor other Investment Data Science team members on machine learning techniques


We believe in building a diverse team with varying qualifications and experiences. As such, we are looking for researchers who can bring not only the technical and quantitative skills, but also unique perspectives to the many questions and problems we are tackling. The ideal candidate is a self-directed, hard-working team player. This person must be intellectually curious and capable of managing the demands of multiple projects at one time.  He/she should be a quick study with high energy. This position is appropriate for individuals with strong analytical capabilities, strong written and verbal communication skills, and an eagerness to apply both in the context of a collaborative investment decision-making process.


Specific qualifications for the position are the following:

  • Advanced academic degree (or equivalent experience) in a quantitative field such as computer science, mathematics, or statistics
  • Strong background in classical machine learning, ideally including at least five years of relevant experience, both academic and commercial
  • Deep experience working with probability, statistics, time-series and cross-sectional analysis, especially with very large data sets
  • Fluency with one or more programming languages including Python, R, Scala, Java, C++/C#
  • Interest in applying computational methods to tackle challenging real-world problems within a domain such as finance and economics


Helpful but not required:

  • Familiarity with finance and asset management
  • Experience working with financial datasets
  • Experience building deep learning models