Data Science Manager

Data Science Manager

Multiple Locations

Essential 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 models
• Partner with Data Engineers and Project Managers to deliver end-to-end solutions
• Partner 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 opportunities
• Build 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 sources
• Uses 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 initiatives
• 3+ years of management experience
• Ability to break down complex business and technical problems into opportunities for analytical study
• Extensive 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 approaches
• Deep knowledge of statistical areas such as ANOVA, multiple regression, timeseries modeling, principal component analyses, decision trees, clustering, etc.
• Experience programming in R, SQL, Python
• Automate 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 datasets
• Significant experience coding and maintaining predictive algorithms
• Superior research, statistical, analytical, processing and mathematical skills with ability to structure and conduct analyses