Senior Scientist - Experimentation

About the Role

Key Responsibilities

• Work on a diverse, cross-functional team (technical and non-technical) to help implement high-impact, data-driven solutions to address business challenges in vehicle brand marketing teams, ensuring that project deliverables fulfill business needs and timing.
• Bring strong contextual business knowledge and domain expertise in advertising, paid media, and digital marketing, including understanding key metrics such as CTR, CPC, CPM, and conversion rates, as well as familiarity with industry measurement platforms.
• Develop and apply advanced modeling techniques, including causal inference methods (e.g., difference-in-differences, synthetic control), machine learning, and other statistical approaches to optimize media strategies, understand incremental impact, and improve ROI on paid media investments.
• Apply advanced data-driven modeling techniques to inform decision making and solve complex business problems: preprocessing multisource data; and presenting complex information using data visualization techniques.
• Demonstrate an ability for using data, analytics, and insights driving business-impact.
• Exhibit a perspective of constrained curiosity, asking why, and suggesting experimentation to drive incremental impact.
• Effectively communicate through telling a succinct, data-driven story in any forum, tailoring delivery to a wide range of stakeholder levels from analyst to senior executive.
• Build predictive models and machine-learning algorithms.
• Analyze large amounts of information to discover trends and patterns.
• Undertake preprocessing of structured and unstructured data.
• Present complex information using data visualization techniques.
• Propose solutions and strategies to business challenges that drive business impact.

Requirements

• 5+ years of experience as a data Scientist or in a related role, developing, deploying, and monitoring performance of large analytic data sets and machine learning based models.
• 2+ years of experience working with site/clickstream measurement, A/B testing, and various industry measurement media platforms.
• Expertise with fundamental statistical concepts and considerations for selecting an appropriate modeling technique.
• Proficient in Python/R (modeling) and SQL/Spark (data).
• Ability to prioritize and manage multiple tasks and projects at once without sacrificing quality.
• Proficient in one or more analytical methodologies such as numerical optimization, econometric modeling and forecasting, machine learning, and text analytics.

Nice to Have

• Master's Degree or Ph.D. in Marketing Science, Econometrics, Statistics, Psychometrics, (Bio)Statistics, Applied Mathematics, Operations Research, or another quantitative field.
• Experience with cloud compute platforms such as Azure with Databricks experience being especially valuable.
• Familiarity with 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.
• Prior experience with paid, earned, and owned media and/or marketing analytics.

Qualifications

• Experience with site/clickstream measurement, A/B testing, and industry measurement media platforms.
• Strong understanding of statistical concepts and modeling techniques.
• Proficiency in Python/R and SQL/Spark.

Benefits & Perks

• GM offers a variety of health and wellbeing benefit programs including medical, dental, vision, Health Savings Account, Flexible Spending Accounts, retirement savings plan, sickness and accident benefits, life insurance, paid vacation & holidays, tuition assistance programs, employee assistance program, GM vehicle discounts, and more.
• Compensation includes an expected base salary of $125,000 - $205,100, with bonus potential based on company, job level, and individual performance.