Nuveens Client Insights & Analytics organization is focused on turbo-charging distribution through digital and analytics capabilities across sales, service, marketing, and product. We are focused on:
establishing a deep understanding of our business and clients through customer research and analytics to redefine the metrics that matter
building data-driven sales and marketing practices enabled by segmentation and prescriptive modeling to target and serve financial advisors based on their needs and preferences
developing scalable engagement models incorporating digital to enhance our effectiveness, productivity, relevance and return on investment
expanding digital capabilities to deliver a distinctive, omni-channel client experience seamlessly integrating digital and human interactions
The Data Scientist will join Nuveens Data Science practice to conduct analysis and build models aligned to our distribution strategy to enhance the effectiveness of client acquisition, development and retention. They will collaborate across the Global Distribution, Product, Marketing, Client Servicing and Technology teams and be a part of Nuveens Client Analytics efforts.
Conduct statistical analysis, build models, and surface insights that enable the client facing organization to make better decisions
Develop models and analyze interaction and transaction data to identify patterns, uncover opportunities, and create executable analytics to drive revenue
Identify and use appropriate investigative and analytical technologies to interpret and verify results
Develop statistical and machine learning techniques to build models that improve how we engage with clients
Apply and learn a wide variety of tools and languages to achieve results (e.g., Python, R, SPSS, Hadoop)
Requires 3 years experience in advanced analytics, model building and deployment
Hands on experience designing, building and evaluating practical machine learning solutions to solve business problems
Advanced degree in relevant field required (computer science, statistics, applied mathematics)
Experience in normalization of data, data mining, and tools
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