Senior Data Scientist

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 measurement initiatives; develop KPIs and learning plans for LiveRamp customers in support of their marketing objectives.

• Develop, implement, and maintain analytics systems, tools, and frameworks that enable efficient analysis for customers.

• Design approaches for observational analytics, including mining multi-dimensional datasets, and experimental methods to build templates and libraries of analysis that can scale across various customer use cases and verticals.

• Consult and advise clients and internal teams on audience strategy, experimental design, data requirements gathering, and post-campaign measurement using statistical and mathematical concepts to drive business solutions and actionable recommendations.

• Get hands-on with customer data to drive insights, answer key business questions, and enable campaign optimization.

• Identify and communicate areas to improve effectiveness, efficiency, and productivity.

• Create compelling presentations and effectively communicate complex analytical concepts to business and technical audiences.

• Speak and present both internally and externally, leveraging data to tell a story.

• Build up a strong understanding and expert knowledge of the various data sources brought together for data enrichment and collaboration.

• Work directly with customer stakeholders to gather requirements, communicate updates and findings, and build relationships.

Your team will: 

• Work closely with product and engineering teams to support and understand the cloud infrastructure that runs the models.

• Dive deep with internal and external clients to understand their measurement and analytical needs and translate them into product improvements.

• Help design new offerings in the analytical space.

About you: 

• Bachelors with 5+ years of experience, Masters with 3+ years of experience, or PhD with 0-2 years of experience in a data science related field (mathematics, statistics, computer science, etc.).

• Strong command of probability and statistics.

• Fluency in data and statistical analysis using SQL, Python, and PySpark.

• Strong command of data science toolkits (Pandas, NumPy, scikit-learn, etc.)

• Understanding of cloud platforms (AWS, GCP, etc.) and relational databases.

• Strong problem-solving skills.

• Attention to detail and time management, producing high quality, client-ready analytic deliverables across multiple projects and client engagements.

• Experience with ad-tech or curious about the ad-tech market and related measurement.

• Comfortable adapting to the latest tools and curious to explore industry best practices.

• Strong oral and written communication and organizational skills.

Preferred Skills:

• Familiarity with fundamentals of randomized-controlled trials, A/B testing, and mar-tech/ad-tech measurement.

• Excellent communication skills - the ability to work cross functionally, with internal and external stakeholders, to translate product requests into features and analysis into business objectives.

• Strong understanding of large-scale data analysis, fine-tuning of Spark jobs, and optimizing Spark SQL/PySpark models.

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