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Having experience with productizing machine learning models within a cloud service (e.g., AWS, Azure, GCP) and data science environments (e.g., Databricks) is a plus. You have 2-5 years of successful technical experience in the domain of predictive analytics (e.g. data science, machine learning, data mining, and statistics related work); preferably in the Oil & Gas industry.
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Collaborate with data scientists, software engineers, domain experts, and client stakeholders to understand requirements, gather feedback, and integrate machine learning solutions into larger systems or products.
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TRM leverages big data and machine learning to prevent cryptocurrency fraud and financial crime. Our products are trusted by leading companies including PayPal, Visa, FTX, Uniswap, Anchorage, and federal agencies such as the FBI and IRS. Every day, we tackle challenges in data engineering, data science, and threat intelligence to advance our mission to build a safer financial system for billions of people.
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KEYWORDS Machine Learning | Physics | Embedded Systems | Signal Processing | Data Analysis | Nvidia | Electrical Engineering | Cloud Computing | Cuda | TensorRT | Radio Frequency | Transmission Processing | Linux OS | Pytorch | Python | Edge Computing.
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The team uses emerging technologies and methods such as data fabric, machine learning, GenAI, NLP, computer vision, artificial intelligence automation, big data analytics, and cloud native application development to drive innovation, operational efficiency, and provide the data product excellence our customers have come to value and trust us for.
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The candidate should also have experience with multiple modern techniques and algorithms in machine learning and statistical computing such as regression, clustering/segmentation, time-series analysis, recommender systems, predictive analytics, Bayesian modeling, data fusion, supervised/unsupervised learning, decision trees, A/B testing, natural language processing.
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What You'll DoProcess, cleanse, and verify the integrity of data used for downstream analytics productsApply advanced machine learning, graph analytics, data mining, statistical methods, algorithms, and time series models to vast amounts of user data to derive insights based on the knowledge graph (ontology)Participate in strategic planning and be responsible for the team roadmap and its execution.
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Engage with diverse specialists in areas such as data fusion, signal and image processing, analytics, cloud computing, machine learning, modeling and simulation, service architectures, data management and visualization, and pattern recognition.
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Drive cross-functional collaboration across our data science, machine learning, lab informatics, and medicinal chemistry teams to advance our computational infrastructure for our drug discovery and development mission.
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Educational background and completed coursework should encompass one or more of the following: machine learning, statistical modeling, data analysis, data visualization, artificial intelligence, coding and/or data engineering.
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Job Description Support GDIT's FedCiv business areas by developing solutions to deliver capabilities, focusing on Data Science, Machine Learning, and emerging artificial intelligence (AI) capabilities.
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Our mission and team expertise spans beyond software to advanced sensor systems, algorithms, embedded systems, signal processing, and machine learning. We are building and deploying edge software and cloud services for real-time customer facing products as well as internal big data tools.
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The ideal candidate will be adept in navigating the data stack and able to support initiatives in all facets from analytics/data engineering and product analytics to machine learning.
$180,000 - $275,000 a yearFull-timeExpandApply NowActive JobUpdated Today - UpvoteDownvoteShare Job
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Job DescriptionJob DescriptionPosition Overview:We are seeking a highly-skilled AI Curriculum and Chatbot Development Specialist with expertise in Machine Learning, Data Science and Artificial Intelligence, with strong programming skills to develop innovative AI-focussed curriculum for various age-groups and skill levels (from middle school through college-age) and design chatbots to support the learning of this curriculum.
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Bonus if those were open-sourced big data processing frameworks using technologies like Spark, Airflow, Kafka, Flink, Iceberg, Deltalake. By integrating this data with our online systems, we empower multiple business lines, drive critical machine learning models, and fuel fast-paced experimentation.
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machine learning software data processing big jobs Company: Lyft
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