Quant Analytics Associate -Digital

• Deep-Dive Analytics: Conduct rigorous deep-dive analyses of digital journeys, funnels, and feature performance to identify growth opportunities, quantify value (impact sizing), define success metrics, and prioritize analytics recommendations aligned to strategic goals ie CSAT, engagement, deepening, retention.
• Experimentation & Measurement: Work on analytical plans that support A/B testing and experimentation; partner with cross-functional teams to define hypotheses, success criteria, guardrails, and post-test readouts that drive decisive action.
• Data Storytelling & Executive Communication: Translate complex analyses into clear, compelling narratives and visualizations; communicate insights, trade-offs, and recommendations in a way that influences senior stakeholders and accelerates roadmap decisions.
• Data Quality, Integrity & Instrumentation: Ensure data integrity and consistency across analytics platforms; partner with data engineering to improve event instrumentation, data collection, pipelines, documentation, and governance to enable scalable, trusted measurement.
• Strategic Discovery Support: Partner with QUAD and product teams to lead discovery - formulate problem statements, size opportunities, define success metrics, and track outcomes.

• Bachelor s degree required in data science, mathematics, statistics, econometrics, engineering, economics, or related quantitative fields with 3+ years of experience analyzing customer experiences and/or digital products/usage, with demonstrated ownership of end-to-end analytics deliverables (problem framing analysis recommendation impact tracking).
• Proven track record of problem-solving using data and building new analytics capabilities (metrics, dashboards, experimentation measurement, analytical frameworks).
• Strong ability to translate quantitative outputs into actionable business insights and influence decisions through structured communication and data-driven storytelling.
• Advanced proficiency in querying large-scale datasets using SQL and/or Python; ability to work effectively with big data platforms and partner with engineering teams.
• Demonstrated expertise in digital analytics (journey/funnel analysis, segmentation, feature adoption, retention).
• Excellent written, verbal, and presentation skills with experience communicating effectively with diverse stakeholders, including senior leaders.

• Hands-on experience with generative AI solutions, including large language models, retrieval-augmented generation, and agentic AI frameworks.
• Experience working in Big Data environments (eg, Snowflake, AWS) and collaborating with data engineering on scalable data products.
• Familiarity with modern analytics platforms and instrumentation practices (event taxonomies, tracking plans, governance).

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