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Sitting at the intersection of quantitative and investment analysis, the professional will be a "hands-on" player/coach building a team of Data Scientists focused on the implementation of Data Science technologies, techniques and methodologies leveraging Artificial Intelligence (AI) and Machine Learning, advanced analytics, and statistical methods.
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Hands-on experience using modern big data pipelines (AWS, Google Cloud, Snowflake)AI/ML models, Natural language process (NLP) and data mining experience Required Experience:Degree within Computer Science, Data Engineering, or a related field.
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You have hands-on experience developing data science solutions using open-source tools and cloud computing platforms. As a Data Scientist at Capital One, you’ll be part of a team that’s leading the next wave of disruption at a whole new scale, using the latest in computing and machine learning technologies and operating across billions of customer records to unlock the big opportunities that help everyday people save money, time and agony in their financial lives.
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San Francisco, California (Hybrid On-site): $174,900 - $199,700 for Data Science PhD. New York City (Hybrid On-Site): $165,100 - $188,500 for Data Science PhD.
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Our consultants bring deep expertise in Data Science, Machine Learning, and AI. Our business value and leadership have been recognized by various market research firms, including Forrester and Gartner.
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Hands-on experience with data engineering/processing frameworks (databricks, spark, dataflow, etc. Research and develop novel statistical approaches and machine learning/deep learning models that add value to manufacturing processes and product performance improvement.
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Responsible for the hands-on development of advanced data science solutions comprising the portfolio developed by the Principal Data Scientist and the technical requirements specified by the Senior Lead Data Scientist.
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We are hiring a Data Science on the credit card acquisitions valuations model development team. Master’s Degree in “STEM” field (Science, Technology, Engineering, or Mathematics) plus 3 years of experience in data analytics, or PhD in “STEM” field (Science, Technology, Engineering, or Mathematics.
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The role will require the ability to flex between technical engineering of the platform and hands on working knowledge creating data science applications as a user of the platform.
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Hands-on experience running common statistical or machine learning procedures, such as descriptive statistics, hypothesis testing, dimension reduction, feature transformation, supervised or unsupervised learning.
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Hands-on experience applying a wide variety of statistical machine learning techniques to real world problems spanning analysis, predictive modeling and optimization on structured and unstructured data.
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Experience developing deep learning models Hands-on experience in training, fine-tuning, and optimizing machine learning models in the context of LLMs and Generative AI Experience deploying transformer-based architectures such as GPT, LLaMa, and BERT Great understanding of classical deep learning models/algorithms: CNN, RNN, LSTM Preferred Experience.
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Our client is looking for a Hands On VP of Data Science to Lead their Data Science teams in Manhattan. Working with smart, talented people across their business and investments to create groundbreaking, next-generation data science applications for n-tier cloud architectures.
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The New York Stock Exchange, a subsidiary of Intercontinental Exchange, Inc. (ICE) presents an opportunity for a full-time hands-on Lead Developer to handle a team responsible for Data Science, AI and ML.
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Applying quantitative techniques, including statistical and machine learning, to uncover latent patterns in the data. We build inferential and predictive models, including machine learning algorithms and AI; we process, integrate and manipulate big data with distributed systems and customer data pipelines; we synthesize results and translate findings into compelling stories that resonate with clients.
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data science machine learning hands on jobs Title: data in Manhattan, NY
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