Google
Senior Business and Marketing Data Scientist
Found: Today
Senior Business and Marketing Data Scientist
About the job
Google's leadership team hand-picks thorny business challenges, and members of BizOps work in small teams to find solutions. As part of this team you fully immerse yourself in data collection, draw insight from analysis, and then zoom out to develop compelling, synthesized recommendations. Taking strategy one step further, you also persuasively communicate your recommendations to senior-level executives, roll-up your sleeves to help drive implementation and check back-in to see the impact of your recommendations.
In this role, you will be the go-to-person for translating business problems from the supported organization or functional area into investigative solutions and insights. You will lead project work including implementing data science solutions, improving data pipelines, developing evaluation metrics, or building statistical models that provide insights to the business. You will create and lead new opportunities through new techniques or capabilities that scale. You will also lead the development process, influence strategy and consensus with stakeholders and team members, and overcome complexity. You will collaborate with supported teams by proactively identifying and translating ambiguous business needs into tractable analyses or evaluation metrics. You will work closely with key products and engineering stakeholders to define strategic insights related to user experience and product impact. You will engage in community contributions to improve and sustain our culture and operations making Google a better place to work.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training. Ireland: €116000 - €119000 (EUR) + 15% bonus target + equity + benefitsLearn more about benefits at Google.Minimum qualifications:
- Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience.
- 4 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis.
- Experience with statistical programming in experimental design and data validation.
- Experience managing end-to-end brand measurement frameworks, including brand lift studies, Marketing Mix Modeling (MMM), or Multi-Touch Attribution (MTA).
Preferred qualifications:
- 8 years of experience delivering marketing analytics, marketing mix modeling, geo experiments, meta analysis, audience segmentation, and propensity modeling.
- Experience working in root cause analysis to ensure that problems are solved at both a tactical and strategic level.
- Experiences with experimental design and supervised/unsupervised machine learning approaches for both regression and classification tasks.
- Understanding of Bayesian approaches and modeling frameworks and applied knowledge of R or Python for statistical analysis and SQL including end-to-end automation of analytics pipelines and workflows.
- Ability to generate practical solutions for marketing analytics problems and use results to drive business change.
Responsibilities
- Provide support in media strategy, measurement and optimization that require expertise in advanced analytics work, with special focus on marketing analytics methods and data science.
- Partner with internal teams in advanced analytics work including experimentation, measurement and modeling. Identify patterns and behaviors that are effective predictors of performance and critical drivers for a successful media plan.
- Deliver customer-centric, data-driven approach, based on a people-based marketing strategy to build, segment, and test audiences for best business results.
- Partner with internal teams to scope, build and deliver strategic initiatives driving all things marketing data, with a strong focus on digital marketing data pipelines.
- Develop evaluation frameworks for large-scale models, new metrics, and investigate anomalies. Frame and solve ambiguous problems by scoping technical priorities and innovating on statistical methods.
