Staff Data Scientist, Marketing Applied Sciences

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Requirements:

• Minimum seven years of experience developing, deploying, and monitoring performance of statistical, AI, and/or machine learning models.
• Master’s Degree or Ph.D. in Data Science, Operations Research, Psychometrics, Marketing Science, Finance, (Bio)Statistics, Econometrics, Computer Science, Applied Mathematics, or other quantitative fields.
• Proficient in Python/R for modeling and SQL/Spark for data, including experience with Pandas, StatsModels, PYMC, TensorFlow, Pytorch, Spark/ML, sklearn, numpy and related packages.
• Desired expertise in marketing analytics and advanced methods including Generalized Linear Models, panel data, mixed effects with Bayesian techniques, classification and regression trees, and simple regressions.
• Prior experience with paid, earned, and owned media.
• Ability to prioritize and manage multiple tasks and projects without sacrificing quality.

Responsibilities:

• Work on a cross-functional team to propose and implement high-impact, data-driven solutions to address business challenges across various business units.
• Apply advanced data-driven modeling techniques to inform decision making and solve complex business problems.
• Create breakthrough solutions, performing exploratory and targeted data analyses.
• Undertake preprocessing of structured and unstructured data and monitor model effectiveness.
• Present complex information using data visualization techniques.
• Collaborate with diverse technical teams to ensure project deliverables meet business needs and timing.
• Identify long-term technical innovations while improving execution efficiency.
• Utilize contextual business knowledge and expertise in advertising and media to drive consumer demand and customer engagement.
• Exhibit a perspective of constrained curiosity, asking why, and suggesting experimentation to drive incremental impact.
• Tell succinct, data-driven stories tailored to various stakeholder levels, from analysts to senior executives.
• Ensure high delivery quality through the creation of internal structures and frameworks to manage accountability and develop staff.