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We are interested in a variety of topics including data visualization and interactivity, applied machine learning and AI, AI bias and trust, big graphs, natural language processing, data mining, big data management, and heterogenous data analytics.
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This high performing team works with clients to implement the full spectrum of data analytics and data science, from data querying and data wrangling to data visualization and dashboarding, to predictive analytics, machine learning, and artificial intelligence as well as robotic process automation (RPA.
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Databricks Certifications (ie: Data Engineer Associate; Data Engineer Professional, Machine Learning Associate, Machine Learning Professional) Design and modernize large scale data and analytics programs that promote organizational intelligence, provide embedded capacity, and implement as-a-service based subscription models at scale.
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Excellent proficiency in Python programming and one or more deep learning libraries (PyTorch, TensorFlow, JAX, etc.) Build and fine-tune deep learning and machine learning models, with an emphasis on large language models.
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Familiarity with Artificial Intelligence/ Machine Learning, Robotics Process Automation and Blockchain and their impact on the regulatory environment. NET (C# or VB), R, Python proficiency.
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Has demonstrated proficiency in one or more sub-disciplines of data science, like causal inference, forecasting, simulation, optimization, machine learning, or deep learning, geospatial.
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The Clinical Data Science team at Eisai, Inc., is expanding its capabilities by acquiring in-house talent related to bioinformatics, machine learning, and big data analytics for the Oncology and Neurology clinical programs.
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The initiatives will include both short-term and long-term research in areas such as artificial intelligence, machine learning & data mining, natural language processing, big data systems, information and data visualization, and social & cognitive science.
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Experience with data modeling, data prep and machine learning tools like Alteryx, RapidMinder, RStudio and Tableau Prep. Experience with statistical and mathematical modeling, artificial intelligence and machine learning software and methods.
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Knowledge of predictive modeling, statistical sampling, optimization, machine learning and artificial intelligence techniques. Python and PySpark serve as our primary programming languages, in addition to SQL for extracting data from Hadoop and other databases.
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Responsible for the administration, configuration, and optimization of the Databricks platform to enable data analytics, machine learning, and data engineering activities within the organization.
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Databricks Implementation: Utilize Databricks for advanced data processing, machine learning, and big data analytics, integrating with Snowflake for seamless data access and analysis.
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Contribute to the firm's success by providing expert legal counsel in patent matters, particularly in software, e-commerce, artificial intelligence, and machine learning. Technical knowledge of artificial intelligence and machine learning is highly desirable.
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Applicants are expected to develop and sustain a research program in one or more of our core areas: high-performance computing, scientific and numerical computing, modeling and simulation, discrete algorithms, and large-scale data analytics (including machine learning and artificial intelligence.
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Statistical analysis, predictive modelling, simulation, machine learning, and artificial intelligence. Guidehouse's Data Science team helps clients maximize the value of their data and automate business processes.
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