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deadline. Strong written and interpersonal communication skills. Preferred Qualifications: - Experience with quasi-experimental econometric methods for causal inference. - Experience with health care claims
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activities from exploratory analysis, visualization, and discovery to prediction, validation, quantification of uncertainty, and inference. To thrive in this role, the ideal candidate will combine a rigorous
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modalities to leverage Lab data streams, expanding our geographic and taxonomic coverage, and developing novel approaches to strengthen inference for critical conservation planning and decision-making
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streams, expanding our geographic and taxonomic coverage, and developing novel approaches to strengthen inference for critical conservation planning and decision-making applications. The Senior Data Science
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a PhD in agricultural and resource economics, economics, or related field by the start of the position. The ideal candidate has a strong quantitative background (econometrics and causal inference
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a deep theoretical understanding of AI, practical proficiency in using AI will be considered in the selection process. A background in causal inference using econometric methods that can be used