Careers
Active job postings for Conlan Scientific.
Job: Software Engineer in Bethesda, MD
Conlan Scientific is a full-stack financial data science development services firm. We focus primarily on quantitative investment strategy development for funds, banks, and lenders. #### Technical Requirements + Bachelor's or greater in a quantitative field. + Extensive Python programming experience. We work primarily in Python. + Computer science knowledge. Applicants … Read more ...
Internship: Financial Data Analyst in Bethesda, MD
Conlan Scientific is a full-stack financial data science development services firm. We focus primarily on quantitative investment strategy development for funds, banks, and lenders. #### Technical Requirements + Bachelor's or Master's candidate in a quantitative field. + Python or R programming experience. We work primarily in Python. #### Nice-to-haves + … Read more ...
[Closed] Job: Financial Data Engineer in Bethesda, MD
Conlan Scientific is a full-stack financial data science development services firm. We focus primarily on quantitative investment strategy development for funds, banks, and lenders. #### Technical Requirements + Bachelor's or greater in a quantitative field. + SQL mastery. We move large workloads through SQL-based data stores. + Expertise in various … Read more ...
[Filled] Job: Financial Data Scientist in Bethesda, MD
Conlan Scientific is a full-stack financial data science development services firm. We focus primarily on quantitative investment strategy development for funds, banks, and lenders. #### Technical Requirements + Bachelor's or greater in a quantitative field. + Extensive Python programming experience. We work primarily in Python. + Machine learning experience. Financial … Read more ...
Blog
Data science discussions and general updates from Conlan Scientific.
Events
Your guide to events we host and sponsor around the D.C. area.
Interactive
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Press
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Team
We believe good data scientists are great software developers. Our team of professionals is ready to solve your financial data challenges.