Job Description
The SpringCube team curated the following job opportunity to help you in your job search. Explore the position below to find your next career move.
Company Overview
A leading global visual discovery platform helps millions of people discover creative ideas, explore possibilities, and plan meaningful experiences. The organization is focused on building an inspiring and innovative platform while fostering a flexible, inclusive environment where employees can do their best work.
The organization is also embracing Artificial Intelligence as a powerful tool to augment creativity, improve productivity, and enhance decision-making. Employees are encouraged to use AI thoughtfully while maintaining strong judgment, transparency, and adherence to applicable governance and safety requirements.
What You’ll Do
- Lead the Monetization Data Science execution roadmap across four strategic pillars: single source of truth and funnel, segmentation, input-metrics cadence, and democratized analytics.
- Develop integrated program plans with clear milestones, success measures, dependencies, and accountability.
- Coordinate Platform, Data Engineering, Monetization Engineering, and Data Science teams to productionize core data tables, governance processes, reliability mechanisms, and scalable pipelines.
- Partner with Engineering teams to address instrumentation and observability gaps, particularly across delivery-funnel systems.
- Drive workflow automation to reduce manual intervention in recurring data workflows and program operations.
- Build durable mechanisms for monitoring, alerting, dependency tracking, and operational visibility.
- Scale self-service analytics through dashboards and partner-facing tools that establish a shared language for metrics and enable rapid diagnostics and opportunity identification.
- Establish and improve business review cadences that help teams set goals and remain accountable for controllable input metrics rather than focusing exclusively on revenue outcomes.
- Lead targeted cross-functional deep dives into areas such as influencer populations, auction density, and demand, translating findings into clear decisions and action plans.
- Use Generative AI as a default operating model for program execution by producing AI-assisted drafts of program artifacts and modernizing high-effort workflows.
- Apply AI to intake triage, status synthesis, action and decision extraction, risk tracking, dependency management, and stakeholder communications.
- Prototype data-driven solutions, dashboards, internal tools, and workflow helpers using AI coding assistants and other AI-enabled development approaches.
- Apply appropriate AI governance, risk management, data-handling practices, output validation, and safety-by-design principles before deploying AI-assisted workflows broadly.
What We’re Looking For
- Proven ability to independently lead multi-team, multi-quarter technical programs while resolving ambiguity, driving decisions, and delivering measurable outcomes through influence.
- Strong cross-functional leadership skills with experience partnering closely with Product and Engineering teams while aligning Design, Sales, Product Marketing, Core, Platforms, and Data stakeholders.
- Experience building trusted metrics, single sources of truth, and operational cadences that encourage organizations to focus on leading indicators and rapid diagnosis.
- A strong track record of building durable operating mechanisms, including cadences, dashboards, decision logs, RACI/DRI structures, and other systems that reduce operational effort and improve execution velocity.
- Excellent risk and dependency management capabilities, including the ability to anticipate cross-organizational failure modes, maintain stakeholder alignment, and escalate issues with clear recommendations and options.
- Demonstrated ability to use Generative AI to accelerate planning, program operations, analysis, and stakeholder communications.
- Strong judgment when reviewing, validating, and refining AI-generated outputs.
- Experience transforming repeatable program activities into durable, low-effort workflows and mechanisms.
- Strong AI fluency, including experience with prompting, AI-assisted coding, lightweight scripts and tools, dashboards, data analysis, and AI agents where appropriate.
- Experience operating within AI governance frameworks, including risk assessment, data handling, model and output validation, auditability, and traceability.
- Bachelor’s degree in Computer Science, Engineering, a related field, or equivalent professional experience.
Disclaimer
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- No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
- No Client Relationship: This company is not a client of SpringCube unless stated.
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