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Perform data exploration using a combination of statistical programming languages (including, but not limited R, Python, SQL, SAS) and deploy predictive analytics and machine learning techniques to improve risk prediction, improve reserve, trend and financial forecasting in a manner that is actuarially sound, and enable real-time results and operational efficiencies.
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Minimum of 5 years of experience in game design, with at least 2 years focused on running Live games or LiveOps. Proficient in data analysis and statistical tools (e.g., SQL, R, Python.
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Experience with image analysis, machine learning, and deep learning frameworks Pytorch, TensorFlow-Keras). The incumbent will apply image analysis and machine learning in collaboration with a group of computational and biological scientists to perform translational oncology digital pathology research for programs in clinical development.
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Experience using statistical analysis and computing, machine learning, deep learning, processing large data sets, data visualization, data wrangling, mathematics, and programming.
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Knowledge in use of statistical software packages such as Stata, R, and SAS and understanding of programming languages such as Python. They will work within the UCSF Program for Pharmacoeconomics and Policy Studies (PRoPEPS) led by Dr. Leslie Wilson and in conjunction with Hangar Clinics Nationally, as well as with other research projects in PRoPEPS, but with a large focus on statistical analysis.
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Strong programming skills with proven experience crafting, prototyping, and delivering machine learning solutions into production. Experience with deep learning libraries (e.g. PyTorch, Jax, Tensorflow) and platforms, multi-GPU training, and statistical analyses of observational and experimental data.
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Collaborate with internal and external partners, including Biostatistics, Statistical Programming, Clinical Operations, Clinical Research, Quality Assurance, Global Patient Safety and Risk Management, and Regulatory Affairs; CROs, central and local laboratories, and other vendors.
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Skilled in statistical data analytics, including DOE, JMP, SPC, APC and FMEA, and experienced in Lean Manufacturing/Six Sigma methodologies and practices. We're seeking a highly skilled and experienced R&D Process Engineer to pioneer the development of processes enabling the application of diamonds in AI chips and power electronics.
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Contribute to machine learning and large language model (LLM) pipelines and APIs, including OpenAI (GPT-3.5 and GPT-4), Azure (OpenAI), Anthropic (Claude), and others. Provision of anything you need to be successful - learning tools, hardware, office equipment, software.
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Our Flavor Scientist "Flavorist" is a key member of the R&D team, helping create and enhance flavor formulations to support the development of our food and beverage products. The Flavorist will determine the flavoring needs of each product, synthesize flavors in our R&D lab, and make application recommendations.
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Apply mathematical (geometry, linear algebra, numerical methods, error analysis) and machine learning methods to prototype and develop algorithms for converting raw depth sensor data into point clouds, 2D and 3D tracking like SLAM, 3D point cloud reconstruction, registration, and classification as well as texture projection and completion.
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Familiarity with emerging IAM skills like IAM in Multi-Cloud (AWS, Azure, Oracle, GCP) and Hybrid Environments, Identity Analytics and Machine Learning, Biometrics & Password less Authentication, Adaptive Authentication, Authorization, and Access Controls.
$125,000 - $135,000 a yearFull-timeRemoteExpandApply NowActive JobUpdated Yesterday - UpvoteDownvoteShare Job
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As a Staff ML Ops Engineer at SiriusXM, you will be a key player in our Data Platform Team. Your role will be pivotal in deploying, managing, and optimizing machine learning (ML) models, leveraging advanced tools like Databricks and MLFlow.
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Familiarity with data science/AI, networking technologies (especially network security), and machine learning/artificial intelligence with a focus on hardware security. Knowledge of data science/AI, networking technologies, and machine learning is a plus.
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Our machine learning systems monitor and surface suspicious activity (money laundering, illegal activity and terms of service violations) for agent review. Work with the embedded Machine Learning Engineers on the team and ML platform services to deploy models to the production environment and monitor ongoing performance.
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machine learning statistical r programming jobs Title: data scientist in San Francisco, CA
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