M+E Technology Job Board
Sr. Data Scientist – Marketing and Customer Analytics
Apple
Job Summary
The Product Marketing Analytics team is seeking a Sr. Data Scientist for marketing and customer analytics with significant experience in propensity modeling and consultative applied analytic decision support.
Key Qualifications
Significant experience in logistic regression, with preference for experience also applying other modeling techniques including time-series regression (preferably mixed models), data classification and reduction (e.g. cluster, factor, principle components, decision trees, random forest), Bayesian inference and machine learning techniques (e.g., neural nets, deep learning, Bayesian belief networks)
Fluency in SAS, R, Python, or Spark, with experience writing scripts that are part of a production-type analytic solution
Solid technical database knowledge (Oracle, Teradata, Hadoop, data modeling) and experience optimizing SQL queries on large data.
1-2 years of experience working with Tableau
Passionately analytical and curious with strong out-of-the-box thinking
Outstanding written, verbal, and presentation skills with the ability to develop and present conclusions and recommendation to senior executives
Excellent project management and consultative skills
Able to work effectively on sometimes ambiguous data and constructs within an fast changing environment, tight deadlines and priority changes
Description
Support Product Marketing, Marketing Communications and cross-functional marketing and business teams with analytics for database marketing and customer product and services engagement and marketing mix effectiveness.
Develop customer propensity models, with an emphasis on logistic regression, to optimize marketing communications, support cross-functional partners on personalization initiatives, and understand key drivers of customer behavior.
Develop statistical models, with an emphasis on mixed modeling, to analyze total market investments, to include base and promotional pricing, digital and traditional marketing mix, direct marketing attribution, channel and in-store investments and partner media/pricing/program effects.
Manage modeling projects through all phases, including data preparation, quality and integration, modeling, data visualization and presentation of results and deliverables.
Collaborate with analytic engineering in providing domain expertise to support the building of a custom propensity modeling engine, including interfacing with Turi and advanced propensity and machine learning modeling techniques
Design and develop highly polished and functional Tableau dashboards to support reporting, data discovery and statistical analysis for consultative projects and capability development of propensity and marketing mix methods.
Consult with business partners by conducting regular and ad-hoc business analysis and analytics to provide actionable insights and decision support at tactical and strategic levels.
Manage workflows, requirements and projects in collaboration with IT for analytic engineering tracks or ad hoc data model development
Education
Prefer:
Graduate degree required in Business (with quantitative emphasis), Statistics, Data Mining, Machine Learning, Analytics, Econometrics, Mathematics, Operations Research, Industrial Engineering, or related field
Apple
Job Summary
The Product Marketing Analytics team is seeking a Sr. Data Scientist for marketing and customer analytics with significant experience in propensity modeling and consultative applied analytic decision support.
Key Qualifications
Significant experience in logistic regression, with preference for experience also applying other modeling techniques including time-series regression (preferably mixed models), data classification and reduction (e.g. cluster, factor, principle components, decision trees, random forest), Bayesian inference and machine learning techniques (e.g., neural nets, deep learning, Bayesian belief networks)
Fluency in SAS, R, Python, or Spark, with experience writing scripts that are part of a production-type analytic solution
Solid technical database knowledge (Oracle, Teradata, Hadoop, data modeling) and experience optimizing SQL queries on large data.
1-2 years of experience working with Tableau
Passionately analytical and curious with strong out-of-the-box thinking
Outstanding written, verbal, and presentation skills with the ability to develop and present conclusions and recommendation to senior executives
Excellent project management and consultative skills
Able to work effectively on sometimes ambiguous data and constructs within an fast changing environment, tight deadlines and priority changes
Description
Support Product Marketing, Marketing Communications and cross-functional marketing and business teams with analytics for database marketing and customer product and services engagement and marketing mix effectiveness.
Develop customer propensity models, with an emphasis on logistic regression, to optimize marketing communications, support cross-functional partners on personalization initiatives, and understand key drivers of customer behavior.
Develop statistical models, with an emphasis on mixed modeling, to analyze total market investments, to include base and promotional pricing, digital and traditional marketing mix, direct marketing attribution, channel and in-store investments and partner media/pricing/program effects.
Manage modeling projects through all phases, including data preparation, quality and integration, modeling, data visualization and presentation of results and deliverables.
Collaborate with analytic engineering in providing domain expertise to support the building of a custom propensity modeling engine, including interfacing with Turi and advanced propensity and machine learning modeling techniques
Design and develop highly polished and functional Tableau dashboards to support reporting, data discovery and statistical analysis for consultative projects and capability development of propensity and marketing mix methods.
Consult with business partners by conducting regular and ad-hoc business analysis and analytics to provide actionable insights and decision support at tactical and strategic levels.
Manage workflows, requirements and projects in collaboration with IT for analytic engineering tracks or ad hoc data model development
Education
Prefer:
Graduate degree required in Business (with quantitative emphasis), Statistics, Data Mining, Machine Learning, Analytics, Econometrics, Mathematics, Operations Research, Industrial Engineering, or related field