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Minimum of 5 years of technical experience and 3 years of experience specifically in data science and machine learning. Demonstrated expertise in machine learning, deep learning, and NLP, with a strong portfolio in large language models like GPT-3, BERT, and RAG.Advanced proficiency in Python, with hands-on experience in ML libraries and frameworks such as TensorFlow and PyTorch.
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Preferred Qualifications- Previous experience or advanced coursework in data science, algorithmic engineering, or machine learning. - Basic experience with versioning software such as Git.- Working knowledge of data visualization software such as Tableau, Looker, or Domo.- Fundamental knowledge of statistics with some hands-on experience in A/B testing, regression analysis, and ANOVA testing.
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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.
$190,000 - $230,000Full-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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You have hands-on experience developing data science solutions using open-source tools and cloud computing platforms. Team DescriptionConsumer Identity ML is the data science and machine learning team inside Capital One's AI foundation organization.
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Providing hands-on experience in quantitative research, model development, and data analysis. Bachelor’s, Master’s, or Ph. D. in Quantitative Finance, Mathematics, Statistics, Computer Science, or a related field.
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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.
$242,400 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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If the candidate does not have a background in Actuarial science, the candidate should have 4+ years of industry experience building and analyzing machine learning models and should hold a graduate degree in a technical field such as Statistics, Computer Science, Data Science, Bioinformatics, Physics, Mathematics, Economics or Engineering.
$172,000 a yearFull-timeExpandApply NowActive JobUpdated 10 days ago - UpvoteDownvoteShare Job
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Experience in Data science and Machine learning using Python, R, Java, C#, Spark, AutoML, TensorFlow, Amazon AML, Microsoft machine learning studio, PyTorch, IBM Watson and any graph DB.
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S&P is a leader in risk management solutions leveraging automation and AI/ML. This role is a unique opportunity for hands-on entry-level ML scientists and NLP/Gen AI/ LLM scientists to grow into the next step in their career journey and apply her or his technical expertise in NLP, deep learning, GenAI, and LLMs to drive business value for multiple stakeholders while conducting cutting-edge applied research around LLMs, Gen AI, and related areas.
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As a Data Scientist Lead - VP, you will be hands-on in analyzing large-scale data to create analytical, statistical, and data science models to uncover underlying patterns and drivers to business questions in employee relations, conduct, security, compliance, customer complaints, etc.
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Degree in pharmacometrics, pharmacokinetics, pharmacology, pharmaceutical science, mathematics/statistics, engineering, or related field. The modeling and simulation activities include NCA, population PK/PD, disease progression, statistical and physiologically-based PK (PBPK) modeling, allometric scaling, quantitative system pharmacology (QSP), clinical trial simulations, literature meta-analysis, machine learning/deep learning, and other state-of-the-art quantitative techniques.
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Hands on work with data science, data engineering, or machine learning problems within the PyData world. It’s used for science, engineering, data analytics, deep learning, and more.
$253,000 a yearFull-timeExpandApply NowActive JobUpdated 11 days ago - UpvoteDownvoteShare Job
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The complexity and scale of data being generated has grown significantly and requires strong scientific contributions to integrate different approaches in modeling, statistics, and data science to bring value to the Eisai portfolio.
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Hands-on ML and Data Science background: hands-on modeling and training experience with PyTorch and Tensorflow, NumPy, scikit learn, CUDA (optional) Hands-on software developer experienced with on call and production support.
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A major in Information Systems, Computer Science, Technology Management, AI, Software Engineering, Data Science, Project Management, Business Analytics, Electrical Engineering, Mechanical Engineering, Cybersecurity Governance, Communications, Data Analytics, Management Information Systems, Informatics, Information Technology, Business Management, Management Information Systems.
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