Senior Decision Scientist

Atlanta, Georgia
Not Specified
Mar 10, 2018
May 09, 2018
Job Type
Not Specified
Career Level
Not Specified

The Senior Data Scientist will be responsible to manage key strategic projects like end to end Forecast, Consumer analytics, client analytics for different lines of businesses. They will own and implement solution from Discovery phase to Launch and post launch Measurement phase. This role will provide Thought leadership around Business Analytics as a Product, Automated actionable services to drive Competitive Advantage, share best practice on Forecasting, tools & techniques along with business collaboration & communication.

Additional Responsibilities:

  • Use quantitative and analytical skills along with analysis tools (e.g. SAS, R, etc) to develop ad hoc analyses, forecasts, segmentations, predictive models. Is assigned and conducts advanced statistical studies/techniques on behalf of department.
  • Remains abreast of developments in the field(s) of mathematical analysis, market research and economic analysis by pursuing a program of self-development, participating in professional organizations, interacting with peers, reviewing pertinent literature, and so forth. Incorporates advancements when practicable and cost effective.
  • Identifies and reports on competitive and environmental trends as they effect clients and markets, and endorses strategies to capitalize on opportunities dictated by such trends, and evaluates effectiveness.
  • Builds analytical data sets, including development of programs for the extraction and transformation of data across multiple platforms, including mainframe, server and desktop using tools like SAS, R, Python, SQL, etc.
  • Consults internal clients regarding business requirements, analytical approach and deliverables, project planning and cross-organizational responsibilities.
  • Prepares and presents PowerPoint presentations summarizing analysis findings and recommendations, providing actionable insights to business decision makers. Contribute in multiple, simultaneous projects, working on cross-functional teams in project definition, execution and delivery.
  • Executes all phases of quantitative research projects: identifying the business problem, data exploration, modeling, communication of final results.
  • Participating in the design and development of new products/services, especially in regards to modeling and forecasting
  • Other requirements to include but not limited to:
    • Defining business objective
    • Capture KPIs
    • Test strategy
    • Measurement plan
    • Resource commitment across different teams (Finance, Actuary etc)
    • Training, documentation and communication
    • Prioritization, operationalization and launch of a successful test
    • Apply best practice on tools and techniques
    • Collaboration with Technology team

Basic Qualifications:

  • Bachelors in Statistics, Economics, Industrial Engineering, Applied Mathematics, Actuarial Science or related disciplines
  • 3+ years of experience with statistical modeling and data mining in a business context using large and complex datasets
  • 3+ years experience with SAS, R, Python, SQL, etc
  • 3+ years of modeling experience with logistic regression, OLS regression, time series analysis and survival analysis.
  • 2+ years of experience in creating forecast and forecasting processes

Preferred Qualifications:

  • Masters degree preferred
  • Ability to work independently, prioritize workload, multi-task and follow-up as necessary in a fast paced environment.
  • Insurance industry experience.
  • Analytical ability to evaluate broad range of issues related to the companys financial performance and the impact the external economic environment has on it.
  • Presentation and interpersonal skills, professionalism, flexibility and ability to handle confidential information with discretion.
  • Project management abilities and the ability to interact with associates at all levels.
  • Experience with the following types of statistical analyses: Predictive Modeling, Time Series Analysis, Exploratory Data Analysis, Segmentation and Cluster analysis, Customer Modeling (i.e. Retention, Reactivation, Cross-sell)


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