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Experience with Machine Learning and Artificial Intelligence methods such as Advanced Neural Network Architectures, Regression, Classification and Clustering. Leverage latest techniques in machine learning and generative artificial intelligence.
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Demonstrated skills in large-scale data manipulation, machine learning, and pattern recognition. PhD or Master's candidates with a concentration in a quantitative field such as Math, Statistics, Data Science, Economics, Computer Science, Operations Research, Analytics, Engineering, or Physics.
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Degree in Cheminformatics, Computational Chemistry, Computational Biology or related quantitative field preferred Relevant experience includes: Research experience in the field of small molecule drug discovery within industry or academia (industry preferred) Solid understanding of DMPK principles Proven track record of knowledge and use of ADMET modeling and cheminformatics methods and tools Experience with QSAR/QSPR modeling, machine learning, and AI methods.
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Hands-on experience developing machine learning algorithms in Python, R, C, or a comparable language. to extract business insights and experimentation to solve business problemsData Wrangling: Preparing data for further analysis; Redefining and mapping raw data to generate insights; Processing of large datasets (structured, unstructured)Machine Learning: Understanding of machine learning theory, including the mathematics underlying machine learning algorithms.
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Bachelor’s or master’s degree in data science, computer science, operations research, statistics, applied mathematics, or a related quantitative field and 3+ years of work experience in building machine learning models or PhD in a related quantitative field.
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Specialized experience for this position must include: Experience with data science and analytical methods from conducting machine learning, Natural Language Processing, and technical procedures such as, solution design, implementation, and deployment.
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The current Joint Logistics Common Operating Picture (JLOGCOP) is comprised of dozens of data feeds, both automated and manual, from across DoD. This effort is a custom build of artificial intelligence/machine learning (AI/ML) capabilities into the JLOGCOP in order to leverage the data, enhance decision making, and increase the speed of planning.
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Bachelor's degree in a quantitative or technical field of study (e.g. Applied Mathematics-probability and statistics, formal modeling, computational social sciences, Computer Programming-programming languages, math/statistics packages, computer science, machine learning, scientific computing, and Visualization (e.g. GIS/geospatial analysis, telemetry analysis.
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Bachelor’s degree in Computer Science, Mathematics, Machine Learning, Data Science, or a related quantitative field; Master’s or Ph. D. preferred. Practical experience in deploying machine learning and AI models into production systems (such as Docker), ensuring scalability and performance.
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Please note that Meta may leverage artificial intelligence and machine learning technologies in connection with applications for employment. As a Data Scientist at Meta, you will shape the future of people-facing and business-facing products we build across our entire family of applications (Facebook, Instagram, Messenger, WhatsApp, Oculus.
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Juniors pursuing a Bachelor's degree with a concentration in a quantitative field such as Math, Statistics, Economics, Computer Science, Operations Research, Data Science, Engineering, or Physics.
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Performing quantitative and qualitative analysis and data quality assurance using statistical software and computer programs such as R and Python to extract information and build models machine learning and artificial intelligence.
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Experience with causal inference and causal machine learning techniques, and/or advanced machine learning algorithms such as deep learning, NLP, recommender system, network analysis.
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Artificial Intelligence/Machine Learning Scientists develop and deploy AI and ML technology throughout the company. PhD or Master's candidates with a concentration in a quantitative field such as Math, Statistics, Data Science, Economics, Computer Science, Operations Research, Engineering or Physics and a GPA of at least 3.0 on a 4.0 Scale (or equivalent.
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Ability to translate complex quantitative methods into easily understood results for all levels of business customers. Familiarity with topics like maximum likelihood estimation, linear and non-linear modeling, causal inference, density estimation, optimization, scheduling, time series forecasting, stochastic processes, survival analysis, data blending, data visualization, and multivariate techniques such as PCA, factor analysis, and cluster analysis.
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