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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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Data science, machine learning, optimization models, PhD in Machine Learning, Computer Science, Information Technology, Operations Research, Statistics, Applied Mathematics, Econometrics, Successful completion of one or more assessments in Python, Spark, Scala, or R, Using open source frameworks (for example, scikit learn, tensorflow, torch) Primary Location.
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A degree in Engineering, Statistics, Data Science, Applied Mathematics, Computer Science, Physics, Computational Biology, Computational Chemistry or related quantitative field (Masters or PhD preferred for Senior Manager direct hires.
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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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Our consultants bring deep expertise in Data Science, Machine Learning, and AI. Our business value and leadership have been recognized by various market research firms, including Forrester and Gartner.
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Experience with deep learning frameworks such as Tensorflow, PyTorch, and Keras. In this role, the Staff Data Scientist will work closely with cross-functional teams to design, deploy, and improve scalable machine learning models for edge devices.
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Data Science tools like Knime, Dataiku, AWS SageMaker, statistical languages, and packages, e.g., R, Python, Julia, SAS, data visualization tools and techniques, e.g., Tableau, ggplot, matplotlib, R/Shiny, Qlik, data manipulation tools such as SQL, Spark, PySpark and Hive, cloud environments like Azure and AWS, deep learning tools such as Tensorflow, Keras and PyTorch.
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2+ years of experience in Data Science or a related field, with a focus on building NLP/LLMs or Deep Learning models. Design and implement machine learning algorithms for NLP/LLMs or deep learning models at scale to meet business requirements.
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Demonstrated knowledge of advanced data science toolsets (e.g., Tensorflow, Pytorch, Scikit-Learn, Airflow, Spark) Apply techniques such as classification, clustering, regression, NLP, deep learning (CNN, RNN, GANs), time series forecasting, Bayesian methods to build scalable solutions.
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The role is critical in growing the company as the successful candidate will lead the rigorous data-driven development of new spectroscopy instruments and analytical techniques using your proven skills and experiences in data science and spectroscopic analysis, and also find valuable insights from data sets generated by our winemaking customers.
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The Principal Data Scientist / Applied Machine Learning Scientist Computational Advertising is a critical data science/applied machine learning role that uses cutting-edge machine learning, deep learning, big data mining and optimization techniques to solve the challenging problems from ads relevance, ranking to campaign optimization.
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Exceptional Expertise: PhD in any field plus 12 graduate-level credits in machine learning or artificial intelligence with an emphasis in neural networks & Minimum 2 years' experience designing, researching, or engaged in developing algorithms and software applications for neural networks in Python; solid knowledge of neural networks and deep learning algorithms and applications.
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Design, implement, deploy, and maintain deep learning and Client models using cloud technologies (e.g., Azure Databricks, MLFlows and Azure Client) BS, MS, or PhD in Computer Science, Statistics, Applied Mathematics, Data Science, Economics or related quantitative fields.
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5+ years of work-related experience in Deep Learning and Machine Learning, including deep learning frameworks TensorFlow or PyTorch, GPU, and CUDA experience extremely helpful.
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Support GDIT's FedCiv business areas by developing solutions to deliver capabilities, including Data Science and Machine Learning. where you can elevate your skills in Data Science, Machine Learning, or leading AI techniques and technology expertise into solutions in support of our Government customers.
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