Data Science Manager

Data Science ManagerMultiple LocationsEssential Functions:Provides leadership in advanced engineering, data science and analytics in the development of current or future products, technologies or services.Lead a team of data scientists to design, prototype, implement and test predictive and prescriptive analytic modelsPartner with Data Engineers and Project Managers to deliver end-to-end solutionsPartner with cross-functional teams to identify and explore opportunities for the application of machine learning.Applies artificial intelligence and machine learning techniques to solve complex questions or fuel new business opportunitiesBuild and/or utilize toolsets and set up processes for extracting information from unstructured data streams.Implements data and analytics solutions, through data science techniques, that solve business problems and create business value.Provides technical guidance and mentoring to business insight and visualization teams, as needed.Leads and executes independent quantitative research projects, leveraging data from multiple sourcesUses best practices to understand the data and develop statistical, analytical techniques to build models that address business needs.Required Knowledge and Skills:MS or PHD degree preferred in statistics, applied mathematics, or computer science (machine learning)5+ years with predictive modeling techniques and experience in leading predictive modeling initiatives3+ years of management experienceAbility to break down complex business and technical problems into opportunities for analytical studyExtensive knowledge and experience in data science, including expertise in one or more of: machine learning, big data/data mining, statistics, business/customer intelligence, data modeling, databases, data warehousing, or a similar field.Business application of the following techniques: hierarchical Bayesian, Markov chain Monte Carlo, random forests, generalized boosted models, generalized additive models, neural networks, time-series forecasting, game theory, conditional probabilities or other similar approachesDeep knowledge of statistical areas such as ANOVA, multiple regression, timeseries modeling, principal component analyses, decision trees, clustering, etc.Experience programming in R, SQL, PythonAutomate data wrangling, iterative solution search and operationalization of models, working alongside data architects.The ability to handle missing data through an algorithmic approach such as multiple imputations to enable insights in sparse and messy datasetsSignificant experience coding and maintaining predictive algorithmsSuperior research, statistical, analytical, processing and mathematical skills with ability to structure and conduct analyses