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Data Analytics, Data Science, Statistics, Health Informatics, Biostatistics, Computer Science, Artificial Intelligence, Machine Learning, Applied Mathematics. 0 years of related experience with a Ph. D. in Data Analytics, Data Science, Statistics, Health Informatics, Biostatistics, Computer Science, Artificial Intelligence, Machine Learning, Applied Mathematics.
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At J.Jill, our success is guided by a culture that values the intrinsic art of creativity and the data science required to drive our future growth. J.Jill is a premier omnichannel retailer and nationally recognized womens apparel brand committed to delighting customers with great wear-now product.
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TheMachine Learning Engineerrole requires experience in Azure OpenAI, Large Language Models (LLMs), Python, and proficiency in developing applications using Streamlit, LangChain, Databricks, and Python frameworks like Flask and FastAPI. The ideal candidate will be adept at building scalable machine learning models and deploying them to production environments, and also being part of customer success teams.
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Typical summer projects may involve one or more of the following: numerical simulations; programming for data acquisition and data analysis applications; design and assembly of laboratory hardware, including mechanical, electronic, and cryogenic components; and participation in quantum measurements.
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Educational background in African American History, Social Sciences and/or Political Science, Library Science, or Archival Management. Are you an Archivist with a passion for and knowledge of history related to the modern Civil Rights Movement and Dr. Martin Luther King Jr. and Mrs. Coretta Scott King.
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5+ years of experience in data analysis or data science, with 3+ years focusing on machine learning problems, ideally in a relevant space (KYC, sanctions detection, anti-fraud detection, treasury management, crypto/blockchain data science.
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MA/MS degree in Computer Science, Computer Engineering, Data or Business Analytics, Data Management, or related field. Author, submit, refine, and execute the Data Management Plan for a large multi-site, multi-organizational CDC sexual health program.
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As a Machine Learning Engineer you will research, develop, and support the machine learning models that allow our devices to decode evidence like a detective and define how non-violent crime is eliminated.
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Partner with IT Asset Management teams and IT Product Owners to align discovery trend data with asset lifecycle management data requirements. Establish data quality standards aligning to ServiceNow CMDB health metrics and CSDM.
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Process Documentation: Develop clear and well-documented procedures for payroll data integration and financial reporting, ensuring accurate and consistent information flow as the Finance Director requires.
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Proficient with data, video editing and scout technology platforms (i.e., Opta, Hudl, Sportscode, Wyscout, etc.) Collaborates with Head of Talent ID and Operations Manager to align strategic plan and tactics with the department budget.
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Creating pitch decks, collateral and other sales enablement materials, and where appropriate, partnering with graphic designers to turn written content into visual communication and marketing collateral in tight alignment with the government sales team and product & analytics team counterparts.
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In supporting rule analysis, Teammate should be able to Independently perform sophisticated data analytics that fit to the fraud problem being observed (ranging from classical econometrics to machine learning, neural networks, and natural language processing) in a variety of environments using structured and unstructured data.
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Minimum 10 years' work experience in a marketing capacity (marketing associate, marketing coordinate, associate product manager, product manager, senior product manager, product/technical director, etc.
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Key word search: Python, Master's degree, predictive maintenance, preventative maintenance, relocation package, data science. Develop internal APM product utilizing the latest anomaly detection machine-learning algorithms for predicting machine failures.
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