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Position: Sr. Data Scientist - Marketing
Institution: Prudential Financial, Inc.
Location: Newark, New Jersey, United States
Duties: Develop and maintain consultative relationships with key business stakeholders; Identify, source, transform and join public, proprietary and internal data sources; Model large structured and unstructured data sources (e.g. financial transactional, time-series, text, speech/audio and image); Implement advanced statistical methods for prediction and optimization including a wide variety of machine learning technologies (regression, decision trees/forests, boosted models, clustering, LSTMs, etc.) for purposes including explorative analysis, survival analysis, segmentation, prediction and recommendation systems; Perform analysis and implement solutions that maximize business impact
Requirements: Master's/Ph.D. degree in in Mathematics, Statistics, Engineering, Econometrics, Physics, Computer Science, Actuarial, Data Science, or comparable quantitative disciplines; 1-3 years of academic or industry experience applying a wide variety of statistical machine learning techniques to real world problems spanning analysis, predictive modeling and optimization on structured and unstructured data; Experience using programming languages such as Python, R, or equivalent for statistical modeling of large data sets and the use of SQL for data querying; Well-developed written and oral communication skills with ability to present complex statistical concepts to non-analytical stakeholders (Excel, Word and PowerPoint are a must)
   
Text: Sr. Data Scientist - Marketing Develop and maintain consultative relationships with key business stakeholders; Identify, source, transform and join public, proprietary and internal data sources; Model large structured and unstructured data sources (e.g. financial transactional, time-series, text, speech/audio and image); Implement advanced statistical methods for prediction and optimization including a wide variety of machine learning technologies (regression, decision trees/forests, boosted models, clustering, LSTMs, etc.) for purposes including explorative analysis, survival analysis, segmentation, prediction and recommendation systems; Perform analysis and implement solutions that maximize business impact Master's/Ph.D. degree in in Mathematics, Statistics, Engineering, Econometrics, Physics, Computer Science, Actuarial, Data Science, or comparable quantitative disciplines; 1-3 years of academic or industry experience applying a wide variety of statistical machine learning techniques to real world problems spanning analysis, predictive modeling and optimization on structured and unstructured data; Experience using programming languages such as Python, R, or equivalent for statistical modeling of large data sets and the use of SQL for data querying; Well-developed written and oral communication skills with ability to present complex statistical concepts to non-analytical stakeholders (Excel, Word and PowerPoint are a must)
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