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People in the Innovation & Thought Leadership career track drive asset innovation and marketplace reputation within a specific area of expertise. They grow into thought leadership roles, by increasing the depth of their expertise and reputation as an expert or innovator, and can progress either in role, laterally, or upward.
The Data Scientist Associate Principal will be part of Accenture Operations Data Artificial Intelligence (AI) team. They will be managing and coordinating with the team that identifies and develops advanced analytics statistical models, machine learning methods and solutions for Accenture operations clients to improve various business outcome indicators.
• Successfully develop, conceptualize and test various statistical and machine learning models
• Integrate the outcomes as real time analytics to elevate Accenture’s ability to create value for clients in areas and through means not immediately apparent to clients
• Masters or Ph.D. (Computer Science, Statistics, Engineering, Physics, Mathematics, Economics)
• Minimum 3 years' Data Science experience for Ph.D. candidates and 5 years for Masters candidates
• Minimum 2 years' work experience in one or more of the following domains: F&A, Procurement, Fraud, Infrastructure Analytics, eCommerce, Security, Retail, Supply Chain Health Care, Pharma, Retail
• Minimum 3 years' experience in at least one of the following – Supervised and Unsupervised Learning, Classification Models, Cluster Analysis, Neural Networks, Non-parametric Methods, Multivariate Statistics, Reliability Models, Markov Models, Stochastic models, Bayesian Models
• Minimum 3 years' various statistical and machine learning models, data mining, unstructured data analytics in corporate or academic research environments
• Minimum 2 years' experience in statistical and other tools/languages (SAS, C, C++, Java, Python)
• Minimum 2 years' programming experience (C, C++, Java, SAS, Python)
• Experience leading or collaborating with a team of data scientists in developing and delivering machine learning models that work in a production setting
• Knowledge of UNIX or Linux environments
• Experience working with large data sets and tools like MapReduce, Hadoop, Hive, etc.
• Experience working with large data streaming technologies like Spark, Flink, etc.
• Familiarity with relational databases and intermediate level knowledge of SQL
• Ability to think creatively to solve real world business problems
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