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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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5-6 years of machine learning/deep learning experience within frameworks such as TensorFlow and/or PyTorch. Hands-on experience in various LLM fine-tuning techniques (e.g. LORA), LLM inference frameworks (e.g. vLLM), advanced RAG pipelines.
$190,000 - $225,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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We collaborate as a global team to develop cutting-edge data pipelines, data products, data models, reports, analyses and machine learning applications. Applied knowledge of advanced Machine Learning techniques including Ensemble methods, Deep Learning, Natural Language Processing, and Transformers/GenAI.
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Hands-on experience in machine learning, relational databases, and open source programming languages (Python, R) for large scale data analysis. We’re looking for a Data Scientist that will be responsible for assisting with building strategy and implementing of sophisticated analytical models, including predictive, prescriptive, artificial intelligence, and machine learning.
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Utilize Databricks for large-scale data processing and machine learning tasks. Familiarity with developing machine learning models; experience in data engineering will be advantageous.
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Hands-on experience with large datasets: Extensive experience in building machine learning projects with terabytes of data, demonstrating the ability to handle and process complex data sets effectively.
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You’ll have the opportunity to work alongside other talented engineers, CTO, and data scientists on cutting-edge big data, search, machine learning and analytics problems.
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This is an opportunity to lead the Enterprise Tech Data Management and Privacy while heavily influencing the Enterprise Machine Learning Platform and Enterprise Real-Time Event Processing & Analytics Platform to support the end-to-end development and execution of Amex's data management initiative.
$110,000 - $190,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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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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Experience with Machine learning, Natural Language Processing and Vector Search. The candidate will be working with the following state of art technologies; Solr, Lucene, Natural Language Processing, Machine Learning, Linux, Groovy, Python, Splunk, Prometheus, Grafana, DevSecOps, Jenkins, Maven, Gitlab, Nexus, Ansible, TDD, BDD, JMeter, Selenium, and other open source frameworks.
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Experience leveraging complex data to drive business decisions, hands on experience in data science methodologies (predictive analytics, machine learning, patient level data triggers) using R, Pytong, Databricks and deep knowledge of Qlik, PowerBI, Tableau for visualization.
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5+ years experience in one or more of the areas: machine learning, deep learning, computer vision, NLP, recommendation algorithm, Computational Advertising, Anomaly detection. Lead the design, development, testing, deployment, and support of machine learning (ML) models in a cloud environment.
$156,300 - $209,600 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Strong hands-on expertise in building Data engineering solutions using Apache Spark and Scala. Hands on experience working with Azure Cloud services like Azure Data Factory, Azure Data Lake Storage, Azure Even hub, Azure SQL, Azure Analytical services, Azure Cosmos DB.
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As part of the Data Analytics team, the Lead Data Scientist will work closely with the Data Engineering team and business functions to solve real-world oil and gas midstream problems using machine learning, data science algorithms and artificial intelligence.
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Keywords: product management, product manager, analytics, AI, machine learning, data science, visualization, computer vision. 2+ years’ experience in Data Analytics, Data Science and Data Visualization.
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