FactSet creates flexible, open data and software solutions for over 200,000 investment professionals worldwide, providing instant access to financial data and analytics that investors use to make crucial decisions.
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Position: Principal Machine Learning Engineer
Employer: FactSet Research Systems Inc.
Location: FactSet Research Systems Inc., 45 Glover Avenue, 7th Floor, Norwalk, CT 06850. Remote Position: This position may be performed remotely in the United States. However, remote work is not permitted in the following states: Alaska, Arkansas, Delaware, Hawaii, Louisiana, Montana, New Mexico, North Dakota, South Dakota, and West Virginia.
Position Description: Principal Machine Learning Engineer, FactSet Research Systems Inc., Norwalk, CT: Spearhead innovative projects focused on developing and implementing advanced AI-driven solutions. Work on diverse machine learning models with an emphasis on optimizing techniques for text data classification and natural language processing. Collaborate on projects that leverage AI to transform data insights and enhance domain-specific applications, promoting the integration of various machine learning approaches to achieve superior results. Serve as key technical leader and provide thought leadership on best practices in the field. Engage in cross-functional collaboration with product teams to align AI capabilities with strategic business goals. Role includes staying current with emerging technologies and trends to ensure the continuous improvement and effectiveness of AI solutions. Remote Position: This position may be performed remotely in the United States. However, remote work is not permitted in the following states: Alaska, Arkansas, Delaware, Hawaii, Louisiana, Montana, New Mexico, North Dakota, South Dakota, and West Virginia.
Minimum Requirements: Master’s degree in Computer Science, Mathematics, Statistics, or related technical field and 6 years of related technical experience. Must have 3 years of experience with the following; Node.js; MongoDB; RabbitMQ; a caching technology such as Redis or Memcached; Git; Restful Web Design; working with times series data; Python; Elasticsearch; libraries scikit-learn; Numpy; Spacy; applying ML techniques (SVM, logistic regression, Decision Trees, Random Forest, XGBoost, deep learning such as RNN/LSTM) on text data for domain specific text classifications; NLP processing in mining data, cleaning, pre-processing and transformation; combining multiple ML approaches like active/supervised/semi-supervised learning to achieve a better result; and developing tagged data sets.
Salary Offered: $177,000- $217,000/year.
To apply: Apply online at FactSet Careers Search for Jobs for Req Job Number R32928
This position qualifies for the internal FactSet employee referral program.
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Company Overview:
FactSet (NYSE:FDS | NASDAQ:FDS) helps the financial community to see more, think bigger, and work better. Our digital platform and enterprise solutions deliver financial data, analytics, and open technology to more than 8,200 global clients, including over 200,000 individual users. Clients across the buy-side and sell-side, as well as wealth managers, private equity firms, and corporations, achieve more every day with our comprehensive and connected content, flexible next-generation workflow solutions, and client-centric specialized support. As a member of the S&P 500, we are committed to sustainable growth and have been recognized among the Best Places to Work in 2023 by Glassdoor as a Glassdoor Employees’ Choice Award winner. Learn more at www.factset.com and follow us on X and LinkedIn.
At FactSet, we celebrate difference of thought, experience, and perspective. Qualified applicants will be considered for employment without regard to race, color, religion, national origin, sex, sexual orientation, gender identity, disability, protected veteran status or other characteristics protected by law. FactSet participates in E-Verify
Factset San Francisco, California, USA Office
1 California St Ste 1900, San Francisco, California, 94111-5420, United States, San Francisco, United States, 94111-5420
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