Senior Manager, Data Science Services

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LiveRamp is the data collaboration platform of choice for the world's most innovative companies. A groundbreaking leader in consumer privacy, data ethics, and foundational identity, LiveRamp is setting the new standard for building a connected customer view with unmatched clarity and context while protecting precious brand and consumer trust. LiveRamp offers complete flexibility to collaborate wherever data lives to support the widest range of data collaboration use cases-within organizations, between brands, and across its premier global network of top-quality partners. Hundreds of global innovators, from iconic consumer brands and tech giants to banks, retailers, and healthcare leaders turn to LiveRamp to build enduring brand and business value by deepening customer engagement and loyalty, activating new partnerships, and maximizing the value of their first-party data while staying on the forefront of rapidly evolving compliance and privacy requirements. You will: Lead a portfolio of data science services engagements for strategic customers, from scoping through delivery, ensuring projects meet agreed business outcomes, timelines, and quality standards. Translate client business objectives into rigorous analytical designs and experimentation frameworks that leverage LiveRamp's identity, measurement, and data collaboration capabilities. Own executive‑level client relationships for your projects, including setting expectations, communicating results and tradeoffs, and influencing senior stakeholders using clear, data‑driven narratives. Partner closely with Sales, Customer Success, Product, and Engineering to design and package repeatable data science offerings that drive adoption and revenue for LiveRamp. Build and manage a high‑performing team of data scientists and analytics consultants through hiring, coaching, goal‑setting, and ongoing performance management. Establish and continuously improve delivery standards, best practices, code templates, and documentation for experimental design, modeling, and analytics on LiveRamp's platform. Define and track key performance indicators for your portfolio (e.g., project margin, on‑time delivery, NPS/CSAT, incremental revenue delivered for clients) and use them to guide prioritization. Review and challenge analytical work products, ensuring statistical rigor, appropriate methodology selection, and clear articulation of assumptions, limitations, and business implications. Advocate for clients and delivery teams in internal roadmap discussions, translating emerging client use cases into product feedback and new service opportunities. Contribute to thought leadership by developing case studies, playbooks, and internal training that scale advanced analytics and experimentation skills across Global Services. Your team will: Partner with enterprise customers to design and execute high‑impact data science initiatives that unlock the full value of LiveRamp's platform-ranging from audience and media optimization to incrementality measurement, marketing mix and budget allocation, and advanced customer analytics. Operates as a cross‑functional, hands‑on delivery team that combines statistical and machine learning expertise with deep knowledge of identity, privacy‑centric data collaboration, and the adtech/martech ecosystem. Your team will work day‑to‑day in client data environments and LiveRamp‑connected ecosystems to clean, join, and activate large‑scale datasets. Own the end‑to‑end analytics lifecycle for services engagements: defining hypotheses and success metrics, building robust data pipelines, running models and experiments, synthesizing insights, and packaging recommendations into actionable roadmaps that customers can operationalize. Collaborate closely with other Global Services teams to turn successful bespoke projects into scalable accelerators, frameworks, and best practices that can be reused across accounts and regions. About you: 8+ years of experience in data science, analytics, or applied statistics roles, with at least 3 years leading analytics or data science teams in a client‑facing or consulting environment. Proven track record of delivering measurable business impact through experimentation, predictive modeling, or advanced analytics in domains such as marketing, adtech, retail, financial services, or consumer technology. Strong proficiency with Python, PySpark for data science and exceptional SQL skills suitable for working with large, complex datasets. Strong knowledge of statistical modeling and machine learning techniques, especially regression, classification and time‑series analysis. Experience using modern data and analytics tools and frameworks (for example, Spark, dbt, Jupyter, or BI tools such as Looker or Tableau). Hands‑on experience designing and analyzing experiments (for example, A/B tests, holdout tests, or geo‑tests) and causal inference methods. Demonstrated ability to communicate complex technical concepts to no

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