Principal Engineer-Data Science

Verizon•Alpharetta, GA

1d•Hybrid

About The Position

Responsible for analytics, data gathering, data modelling, data mining, strategic marketing, planning, and financial analysis. The role identifies and recommends acquisition, revenue, and retention opportunities within Verizon’s Consumer base, working with large datasets to solve complex technical problems in collaboration with subject-matter specialists and partner teams. It is accountable for improving key performance indicators through personalized, data-driven recommendations, identifying influenceable customer cohorts, and executing targeted marketing campaigns. The position identifies business problems, develops analytical approaches, sizes potential gains, and presents actionable plans. It drives technical and data-related projects of varying scope and complexity, represents the organization’s strategy with Verizon business units, vendors, and executives, and uses data visualization and other data-gathering methods to inform strategic direction and team decisions. This is a hybrid role with a defined work location that includes working from home and at least three days per week in the office, as set by the manager. Employees must comply with hybrid work policies. Scheduled weekly hours are 40.

Requirements

Bachelor’s degree or foreign equivalent in Technology Management, Business Analytics, or a related field, and 6 years of progressive, post-baccalaureate experience in the job offered, as Manager - AI Science, Manager - Business Intelligence, or in a related/similar position; alternatively, a Master’s degree or foreign equivalent in the stated fields and 4 years of experience in the listed areas.
Experience producing dashboards, reports, and presentations to evaluate the impact of real-time, decision-based CRM efforts.
Experience partnering with channel leads and functional teams to supplement quantitative analysis with qualitative feedback.
Experience designing, building, automating, and maintaining well-structured data marts and proactively addressing data quality, accuracy, and completeness issues.
Experience resolving data issues and combining data from multiple systems using SQL, SAS, and/or Tableau.
Experience performing data analysis and developing conclusions and recommendations for stakeholder teams.
Experience ensuring delivered solutions’ business and technical architecture matches business requirements and supports continued experimentation and evolving analytics.
Must reference CBVENS-W.
Ability to work in a hybrid arrangement, including at least three days per week in the office and work from home.

Responsibilities

Perform analytics, data gathering, data modelling, data mining, strategic marketing, planning, and financial analysis.
Identify and recommend acquisition, revenue, and retention opportunities within the Consumer base.
Work with large datasets to solve complex technical problems with assistance from subject-matter specialists and in partnership with other teams.
Improve key performance indicators through personalized, data-driven recommendations.
Identify influenceable customer cohorts and execute targeted marketing campaigns to drive desired outcomes.
Identify business problems and develop analytical techniques to provide solutions to external teams.
Identify opportunities, estimate potential gains, and present actionable plans.
Drive multiple technical and data-related projects of varying scopes and complexities.
Work with other Verizon business units, vendors, and executives to represent and drive the organization’s strategy.
Use data visualization and other data-gathering methods to provide internal strategic direction or guide decisions for other teams.
Produce dashboards, reports, and presentations to evaluate the impact of real-time, decision-based CRM efforts.
Partner with channel leads and functional teams to supplement quantitative analysis with qualitative feedback.
Design, build, automate, and maintain well-structured data marts; proactively address data quality, accuracy, and completeness issues.
Resolve data issues and combine data from multiple systems using tools such as SQL, SAS, and/or Tableau.
Analyze data and develop conclusions and recommendations for stakeholder teams.
Ensure delivered solutions’ business and technical architecture matches business requirements and supports continued experimentation and evolving analytics.