Utilize a blended approach of statistical, computer science and business knowledge to develop new approaches to lead analysis and interpretation of large data sets to develop data driven recommendations to solve business challenges. Collaborate with multi-disciplinary teams to gather and analyze structured and unstructured data using scientific methods and developing and testing models used to predict customer behavior. Develop data strategy to maximize use of data as an enterprise asset.
Understand and prioritize business problems and identify ways to leverage data to recommend solutions to business problems. Organize and synthesize data into actionable business decisions, focused on insights. Visualize complex data sets, draw conclusions and relationships, and develop actionable recommendations. Provide insight into customer behavior, trends, financials and business operations through data analysis and the development of business intelligence visuals.
Work with advanced business intelligence tools such as complex calculations, table calculations, geographic mapping, data blending and optimization of data extracts. Properly use supervised and unsupervised algorithms, linear and non-linear predictive models, and optimization techniques. Develop rapid prototypes to help assess strategic opportunities and future business intelligence capabilities.
Develop dashboards and prepare executive level (or targeted audience) presentations to clearly articulate the results of the analysis. Explain, through strong communication skills and analytical analysis, the business value and expected impact of the work. Clearly and fluently translate technical findings to a non-technical team with quantified insights.
Collaborate with Team Members to utilize knowledge in analytics and statistics to help the business explore new, creative ways to utilize data and make informed decisions.
- Master's degree in Computer Science, Mathematics, Statistics, or similar subject.
- 5-7 years’ Job-related experience with applied analytics
- Deep expertise and experience with statistical data analysis such as linear models, multivariate analysis, and sampling methods.
- Applied experience with machine learning on large datasets.
- Experience articulating business questions and using mathematical techniques to arrive at an answer using available data.
- Experience translating analysis results into business recommendations.
- Experience with Python, Java, C++ or similar language as well as experience with R and/or SAS. Exposure to Hadoop, MapReduce, Spark, Hive or other Big Data Platforms.
- Fluency in SQL.
- Multivariate experimental design.