Job Description
Business Data Scientist, Global Business Strategy and Operations corporate_fare Google place Mountain View, CA, USA bar_chart Mid Mid Experience driving progress, solving problems, and mentoring more junior team members; deeper expertise and applied knowledge within relevant area.
Minimum qualifications:
Master's degree in a quantitative discipline such as Statistics, Engineering, Sciences, or equivalent practical experience. 3 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a relevant PhD degree. Preferred qualifications:
4 years of experience using analytics to solve product or business problems, coding (e.g., Python, R, SQL), querying databases or statistical analysis, or a relevant PhD degree. Experience in designing and implementing causal inference methods. Experience building and maintaining BI infrastructure, dashboards, and data pipelines. Proficiency in database querying (SQL). Excellent communication, presentation, and stakeholder management skills, with the ability to explain concepts to audiences. About the job Please note that the compensation details listed in US role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about . Responsibilities Lead the analytics, data infrastructure, and measurement methodology of seller incentives, designing data pipelines and tooling to executing advanced statistical analysis and generating insights. Partner with Strategy and Operations and Finance stakeholders, who own the end-to-end processes for incentives design, to translate high-priority business problems into clear, data-driven strategies and decisions. Own the full data science lifecycle, using your causal inference, predictive modeling, and descriptive analytics expertise to build frameworks that solve business problems. Manage ambiguous problems working with datasets, using SQL, R, and Python to find creative, scalable solutions and build automated data pipelines, BI infrastructure, and dashboards from the ground up. Act as a data storyteller and partner, translating findings into compelling narratives and recommendations that influence and align executive leaders and teams (Strategy, Product, Engineering).
