-
ID
#52923265 -
Salary
USD TBD TBD -
Source
Emergent Holdings -
Date
2024-11-20 -
Deadline
2025-01-19
Manager, Actuarial & Data Science Services
Michigan, Lansing, 48901 Lansing USASUMMARY: Manages a team responsible for assembling and managing data to facilitate a variety of Actuarial and Data Science functions including predictive modeling projects, advanced actuarial analysis, etc. The team works with other Actuarial and Data Science resources and Information Technology to deploy predictive models into a production environment to support key business functions. The team creates and maintains applications that leverage Actuarial and Data Science information. The manager participates with an interdepartmental group to develop and maintain the enterprise data strategy. RESPONSIBILITIES/TASKS: Manages collecting and organizing of data from various internal and external sources to prepare analyses and data sets for report generation, statistical analysis and data mining projects. Establishes ownership of key data fields: Creates, classifies and documents data definitions. Creates, implements, and assesses appropriate data controls (e.g. reasonability checks, dollar/count controls, field requirements). Provides data necessary to measure the performance of models that are in production. Assists with building model monitoring reports. Develops, deploys and maintains diagnostic reports, dashboards and scorecards that assess and monitor enterprise, operating company and business unit performance and results on an ongoing basis. Leads the data group as it assists the rest of the Predictive Modeling team working with Information Technology, Project Management and the Business Units on the implementation efforts required to move completed predictive models into a production environment to support key business processes. Ensures processes related to deploying models or creating applications are coordinated with IT best practices. Prepares and delivers reports and presentations with a level of detail appropriate for the intended audience. This includes the creation of appropriate documents (PowerPoint presentations, etc.) to be used in the communication of Actuarial and Data Science initiatives. Maintains an understanding of predictive analytics techniques necessary to successfully lead analytics data assembly efforts. Keeps abreast of data developments within the industry and transfers knowledge to the rest of the data team. Active participant in the data center of excellence. Manages data team and participates in creation of departmental strategy. Responsibilities include budgeting, balancing workloads, creating efficiency through automation, special projects, quality improvement and oversight, acting as a subject matter expert and mentor and continuously evaluating toolset and methodologies. DIRECTION EXERCISED: Directly supervises exempt and non-exempt staff in accordance with company policies and applicable Federal and State Laws. Responsibilities include but are not limited to effectively interviewing, hiring, terminating, and training employees; planning, assigning and directing work; appraising performance; rewarding and counseling employees; addressing complaints and resolving problems; supporting and encouraging the engagement process. This position description identifies the responsibilities and tasks typically associated with the performance of the job. Other relevant essential functions may be required. EMPLOYMENT QUALIFICATIONS: SKILLS/KNOWLEDGE/ABILITIES (SKA) REQUIRED: Knowledge in quantitative analytics, including experience using statistical analysis software. Demonstrated skills in a variety of database platforms, data acquisition techniques, business intelligence and analysis tools. Demonstrated ability to use programming to engineer solutions to tasks. Ability to manage projects and determine efficient and cost effective means to manage and improve data. Demonstrated skills with process development and governance of data standards. Expert level understanding of data architecture as it relates to business processes Solid understanding of statistical principles