WW RCC Data Scientist

In this role, you will define how we measure the performance of automated customer interaction systems, build analytical frameworks to compare outcomes across interaction models, and deliver insights that determine where automation creates value and where human expertise remains essential. You will work with structured operational data and unstructured conversation data at scale, applying NLP, experimentation, and statistical modeling to drive decisions that directly impact revenue, cost, and customer experience.You will be a part of the World Wide Analytics team within Retail Contact Center, partnering closely with engineering, operations, and product teams to measure and optimize performance across our global contact center operations. Establish KPIs, design and measure controlled experiments to optimize automated customer interaction systems across voice and chat channels, evaluating performance, ROI, and customer experience impact using structured and unstructured data including transcripts, logs, and feedback. Apply NLP techniques to analyze conversation data, extracting insights on customer sentiment, interaction quality, resolution effectiveness, and emerging issues to identify improvement opportunities and refine the balance between automated and human-assisted experiences. Build analytical frameworks that enable rigorous comparison of customer outcomes, cost efficiency, and experience quality across different service delivery models — informing strategic decisions on where automation adds value and where human expertise is essential. Apply statistical and ML techniques to understand customer interaction patterns — including contact drivers, resolution effectiveness, repeat contact behavior, and the comparative economics of different service channels. Partner cross-functionally with engineering, product, finance, and customer experience teams to measure the impact of new customer interaction technologies, translating analytical findings into clear, actionable recommendations for senior leadership to inform strategic decisions on deployment strategy, automation approach, and operational optimization. Build and maintain scalable analytical pipelines, models, and tooling that enable ongoing measurement and monitoring - moving beyond one-off analysis to production-grade analytical solutions. Develop self-service analytical tools—including automated reporting, interactive dashboards, and curated datasets—that empower business stakeholders to access routine performance insights independently. Bachelor’s degree in computer science, mathematics, finance, statistics, econometrics or other quantitative field and 8 + years of professional experience in data science, machine learning, or advanced analytics, with demonstrated ability to translate business objectives into analytical frameworks and measurable outcomes. Hands-on experience with text analytics, NLP, and sentiment analysis — including working with conversation or transcript data at scale. Experience defining and establishing performance metrics and KPIs — particularly for new programs, systems, or technologies where measurement frameworks don't yet exist. Experience designing and analyzing A/B tests and/or experience with quasi-experimental methods (ex. causal inference, diff-in-diff, propensity matching, synthetic controls) to measure the impacts of business interventions. Strong programming skills in Python and SQL for large-scale data manipulation and analysis. Proficiency in data visualization tools (Tableau preferred). Strong communication and storytelling skills — ability to translate complex analytical findings into clear recommendations for both technical and non-technical audiences, including senior leadership. Familiarity with cloud data platforms, particularly Snowflake and AWS. Master's degree in a quantitative field. Previous experience measuring the performance of automated customer interaction systems (e.g., IVR, virtual assistants, self-service channels). Experience in customer experience analytics, contact center operations, or customer interaction measurement. Hands-on experience leveraging LLMs/Generative AI to improve productivity and generate new insights. Previous experience in stakeholder management and large-scale cross-functional project work.

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