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Harding Loevner is seeking a Data Analyst with strong technical skills and relevant industry knowledge to join the Data team. The Data Analyst will be provided the opportunity to demonstrate their technical and programming skills while offering insights regarding clients, competitors, and financial markets in support of business decisions across the firm. The position will allow an individual to further develop their natural curiosity for organizing, manipulating, reformatting, validating, harmonizing, and interpreting data. The Data Analyst will contribute to client proposals, marketing materials, and client deliverables with an emphasis on leveraging the firm’s attribution, portfolio accounting, and client relationship management systems. Furthermore, the Data Analyst will maintain database tables and queries and manage updates to consultant databases, prospect proposals, and questionnaires, while proactively seeking out and participating in efforts to refine the team’s and the firm’s processes. A Data Analyst interested in automation will strengthen their skills by assisting in the automation of manual data management activities while also participating in data visualization projects.  

This role is suited for candidates who have:

• Up to three years of related experience 

• Bachelor’s degree in a Computer Science, Math, or another quantitative field 

• Strong Microsoft Excel skills

• Fluency in one or multiple programming languages such as SQL or Python (pandas/NumPy stack or REST API) 

• Familiarity with financial metrics, mutual funds, separately managed accounts, composites, international investing, and the asset management industry preferred 

• Factset or other attribution system experience preferred


As an equal opportunity employer, Harding Loevner believes that its pursuit of diversity, equity, and inclusion will strengthen its ability to serve its clients effectively and sustain its success through superior decision-making leading to superior investment outcomes. Harding Loevner celebrates differences among employees in personal attributes, background, and experience as a means to improve collaboration and mitigate cognitive biases.