General Motors · 3 days ago

Data Scientist, Marketing Applied Science

Austin, TX

Full-time

Onsite

Senior Level

$75K/yr - $120K/yr

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AutomotiveElectric Vehicle

Hiring Manager

Heidi Formella

Insider Connection @General Motors

Discover valuable connections within the company who might provide insights and potential referrals, giving your job application an inside edge.

Responsibilities

Work on a cross-functional team to help implement high-impact, data-driven solutions to address business challenges in the vehicle brand marketing teams.

Apply advanced data-driven modeling techniques to inform decision making and solve complex business problems: Performing exploratory and targeted data analyses. Undertake preprocessing of structured data. Monitor and sustain model effectiveness. Present complex information using data visualization techniques.

Work with diverse technical teams and provide data and analytical insights to ensure project deliverables fulfill business needs and timing.

Possessing contextual business knowledge and functional domain expertise in the advertising and media space, showing a proven ability of using data, analytics, and insights to add revenue and business-impacting value in this via audience selection and behavioral engagement.

Possess familiarity with marketing media measurement techniques, including MMM, MTA, unified attribution, ROAS level testing, and various industry measurement media platforms.

Exhibiting a perspective of constrained curiosity, asking why, and suggesting experimentation to drive incremental impact.

Exhibiting the ability to tell a succinct, data-driven story in any forum and tailor delivery to a wide range of stakeholder levels, from analyst to senior executive.

Qualification

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PythonRSQLSparkPandasStatsModelsPYMCTensorFlowPytorchSpark/MLSklearnNumpyProblem-SolvingTime ManagementMarketing AnalyticsCustomer Lifetime ValueMedia Mix ModelingMulti-Touch AttributionConsumer ChoiceTime-Series ForecastingHierarchical BayesPanel-Data MethodsCensored/Truncated RegressionsSelection ModelsNon-Random Treatment EffectsHazard/Survival ModelsStructural Equation ModelingItem Response TheoryLatent Class Regression

Required

Master’s Degree or Ph.D. in Data Science, Operations Research, Psychometrics, Marketing Science, Finance, (Bio)Statistics, Econometrics, Computer Science, Applied Mathematics, or another quantitative field

Proficient writing Python/R (modeling) and SQL/Spark (data), working with Pandas, StatsModels, PYMC, TensorFlow, Pytorch, Spark/ML, sklearn, numpy and related packages

Ability to prioritize and manage multiple tasks and projects at once without sacrificing quality

Preferred

Desired familiarity in marketing analytics and advanced methods such as: customer lifetime value, media mix modeling (MMM), multi-touch attribution (MTA), consumer choice (multinomial logit), time-series forecasting, hierarchical bayes, panel-data methods (fixed/random effects), censored/truncated regressions, selection models, non-random treatment effects, hazard /survival models, structural equation modeling, item response theory, latent class regression