About The Position

The Card Marketing Activation & Transformation team is seeking an experienced & highly motivated Analyst that will focus on the development/maintenance of  complexity modeling  that integrates into the build of end- to- end forecast modeling across all key teams involved in the execution of a marketing campaign. This is a data intensive position that requires prior background in building predictive models, data modeling solutions, and forecasting techniques. This role also involves leveraging statistical and machine learning methods to analyze complex datasets, generate actionable forecasts, and optimize business strategies.

Requirements

  • Bachelor’s degree in a quantitative field or 3 years operational/segmentation analytics or other quantitative experience
  • 4+ years of experience in engineering, statistics, mathematics, or another quantitative field 
  • Proficiency in programming languages such as SQL, Python, R, for statistical modelling
  • Experience with data visualization tools such as Tableau and/or Power BI.
  • Strong understanding of data modeling concepts, including relational and non-relational databases
  • Demonstrated ability to query and analyze large datasets to provide meaningful insights.
  • Strong analytical and problem-solving abilities
  • Experience delivering recommendations to leadership
  • Self-starter with ability to execute quickly and effectively
  • Strong communication and interpersonal skills with ability to interact with individuals across departments/functions and with senior level executives

Nice To Haves

  • Ability to understand LLM models and integrate AI capabilities into operational analytics, preferable

Responsibilities

  • Design, build, and validate predictive models to forecast capacity requirements across various teams/roles and predict marketing campaign timelines
  • Assist in the development of  data models and other user tools to support analytics initiatives and ensure data integrity
  • Apply statistical and machine learning techniques for time series analysis and forecasting.
  • Collaborate with business stakeholders to understand requirements and translate them into analytical solutions
  • Communicate model results, insights, and recommendations to technical and non-technical audiences
  • Monitor model performance and recalibrate as necessary to maintain accuracy and relevance
  • Document modeling processes, assumptions, and results for transparency and compliance
  • Stay updated on the latest advancements in predictive analytics, data modeling, and forecasting methodologies
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