Staff Data Scientist, Product Analytics

Adobe•New York, NY

About The Position

Changing the world through digital experiences is what Adobe’s all about. We give everyone—from emerging artists to global brands—everything they need to design and deliver exceptional digital experiences! We’re passionate about empowering people to create beautiful and powerful images, videos, and apps, and transform how companies interact with customers across every screen. We’re on a mission to hire the very best and are committed to creating exceptional employee experiences where everyone is respected and has access to equal opportunity. We realize that new ideas can come from everywhere in the organization, and we know the next big idea could be yours! About Frame.io Frame.io is redefining how video is made. Over 1 million creative professionals around the world rely on us to seamlessly collaborate and bring their stories to life. You’ll join a team of makers, dreamers, and problem-solvers who care deeply about the creative process. We move fast, collaborate closely, and obsess over the details that make products exceptional. As part of Adobe, you’ll also have the resources, reach, and stability of one of the world’s most admired software companies—while working in a product group that operates with the agility of a startup. It’s the best of both worlds: you'll help shape the future of creative collaboration and see your work adopted by millions of users globally. About the role We’re hiring a Principal level Product Data Analyst who is equal parts product analyst and applied data scientist—someone who can build/shape data foundations (instrumentation, modeling, self-serve datasets) and inform strategic product decisions (insights, experimentation, forecasting, storytelling). This role is expected to operate autonomously —you will define the analytics roadmap, define what “good” measurement looks like, and partner closely with Product, Engineering, Data Engineering, and Data Science to deliver trusted, scalable insights and data products. You’ll thrive here if you enjoy building systems and answering hard questions, and you can translate messy ambiguity into clean, actionable decision-making.

Requirements

8+ years in product analytics, data science, growth analytics, or applied analytics roles (principal scope leans toward 10+ with broader ownership).
Expert-level SQL and strong proficiency in Python (or R) for analysis and modeling.
Experience building analytics solutions on modern data platforms (e.g., Databricks/Spark, Amplitude, Looker, and common BI tools).
Proven ability to convert ambiguous questions into technical specs + analytical answers, with excellent requirements writing and stakeholder alignment.
Strong product intuition: you understand user behavior, product levers, and how teams make tradeoffs under uncertainty.
Outstanding communication and influence skills—able to drive decisions with data (not just present it).

Nice To Haves

Experience with event instrumentation (schema design, tracking plans), metrics layers, and data contracts.
Comfort with streaming concepts (Kafka or equivalents) and event-driven architectures.
Forecasting, propensity modeling, recommendations, or causal methods in production decision-making contexts.
Experience building internal analytics “products” (self-serve tools, standardized KPI suites) at scale.

Responsibilities

Product analytics & decision support Own measurement and instrumentation for key product journeys (activation → engagement → retention → monetization), as well as product availability, access, and configuration states, including KPI definitions, metric governance, and executive-ready performance narratives.
Lead deep-dive analyses (funnels, cohorts, segmentation, lifecycle, causal inference where appropriate) to identify opportunities, quantify impact, and recommend product changes.
Design and analyze experiments (A/B, holdouts, quasi-experimental methods) and ensure learnings translate into roadmap decisions.
Build dashboards and reporting frameworks that enable leaders and teams to self-serve insights and move faster.
Data products & analytics infrastructure Partner with engineering and data teams to translate product/business questions into clear requirements for data products, features, and enhancements.
Help build a robust experimentation platform and program from a nascent offering, so other PMs and Engineers can efficiently run reliable tests.
Lead, in collaboration with Data Engineering, delivery of data products that integrate a variety of batch and streaming data sources (e.g. event pipelines, near-real-time product telemetry, CRM/billing enrichment).
Lead the definition and implementation of canonical product health metrics that can be reused by product teams.
Lead a product analytics governance program: define data stewardship and decision rights, formalize metric and event schemas via contracts, implement quality controls and auditability, and ensure analytics practices align with privacy, security, and compliance requirements.
Improve data quality and trust (definition alignment, anomaly detection, reconciliation, lineage awareness) so metrics are reliable in exec and operational workflows.
Strategic partnership & influence Primary contributor to setting and tracking annual and quarterly OKRs Contribute to analytics strategy and vision: what we measure, how we measure it, and what we automate next.
Influence senior stakeholders through crisp storytelling, strong visualizations, and pragmatic recommendations (what to do next, tradeoffs, expected impact).
Mentor analysts and Product Managers on best-in-class instrumentation and analytics plans, in service of raising the bar on analytic rigor, reproducibility, and product-minded thinking (scope depends on level).
Coach product managers and strategists to strengthen their data literacy, apply rigorous and reproducible analytical methods, and consistently ground product decisions in high‑quality quantitative insights