Come join the QB Live Analytics Team as a Senior Data Scientist.
Our team works cross-functionally to improve and accelerate decision-making across the Expert Segment by surfacing data, insights, and strategic thinking that address our most critical customer and business problems.
This role will partner closely with product managers, marketers, and other analysts across the organization.
ResponsibilitiesDeveloping complex data analysis, methodologies, and predictive data models that result in insights to drive business growth opportunities and decision making.Lead experimentation, including hypothesis formulation, test development, insight generation/visualization, and action planning.Interpret and visualize raw data and make it digestible and accessible for business users.Communicates data-driven insights from complex sources and handles significant volumes of data for complex and unique business problems.Define business problems and translate statistical analytics into business intelligence that drives performance.Apply proven methods and hacking skills in working with divergent data types, data scales, and big data (petabytes), to explore and extrapolate data-driven insights using advanced predictive statistical modeling and testing applied to data acquired and cleansed from a range of sources (relational and non-relational NoSQL databases).Provide updates on KPIs to business stakeholders, offering the guidance essential for appropriately interpreting and building on findings, and fully exploiting the insights revealed through the research.Minimum Requirements2-3 years of experience working in product, marketing, web, or other related analytics fields.Highly proficient in SQL, Tableau, and Excel.Ability to derive insights, tell stories with data, educate effectively, instill confidence in recommendations, and motivate others to act on them.Experience with programming languages including R or Python.Ability to pull and manipulate data including building ETL/data pipelines to support analytics use cases with advanced SQL and data warehousing knowledge.Statistical knowledge to guide and support A/B testing or other experimentation frameworks, and interpret the results to draw detailed and practical conclusions.Experience in modeling or business application/evaluation of machine learning.Excellent problem-solving skills and end-to-end quantitative thinking.
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