Associate Data Analyst (project Hire/internal Assignment)

Disney Disney · Media · Lake Buena Vista, FL +2

The Associate Data Analyst will analyze data from computer vision platforms to identify trends, review detection outputs (false positives/negatives), and support operational improvements for safety and efficiency across Disney Parks. This role involves monitoring data pipelines, supporting systems, reporting findings, and validating changes after tuning updates, acting as a bridge between technical teams and project leadership.

What you'd actually do

  1. Data analysis support: Pull, organize, and review data from computer vision platforms to help the team identify patterns in detection behavior and flag emerging trends for review
  2. Detection review: Run structured reviews of detection outputs, log instances of false positives and false negatives, and compile findings into reports that help technical teams understand where tuning is needed
  3. Pipeline monitoring: Perform routine checks on data pipeline health, flag anomalies or gaps in data flow, and escalate issues to senior team members for resolution
  4. Systems support: Carry out defined support tasks for container-based applications in Linux environments, follow documented procedures for routine maintenance, and escalate issues to software engineers when needed
  5. Reporting support: Summarize analytical findings in clear, structured formats that make it easy for senior team members to draw conclusions and present to leadership

Skills

Required

  • 1+ years of experience in data analysis, project analysis, or a technical analytical role.
  • Working knowledge of data generated by computer vision or machine learning models, including familiarity with how detection outputs are evaluated and interpreted.
  • Familiarity working in a Linux environment, including a working understanding of containers (Docker); exposure to orchestration tools such as Kubernetes (K8s) is a plus.
  • Ability to look at a set of data and begin to identify patterns or trends, with a desire to develop deeper analytical skills over time.
  • Familiarity with data tools for visualization and analysis (e.g., Tableau, Power BI, SQL, Python or R); willingness to learn tools used by the team.
  • Expert in Excel, including VLOOKUPs, pivot tables, charts, and complex formulas, with strong SQL skills for working with large datasets.
  • Ability to independently analyze data and translate insights into clear, actionable recommendations.
  • Proven ability to create clear, visually compelling presentations and tailor messaging for global audiences, including navigating language barriers and working with diverse, cross-functional stakeholders.
  • Experience presenting complex findings to a range of audiences, including technical teams, operations leaders, and internal and external executives—effectively simplifying content for non-technical stakeholders.
  • Good written and verbal communication skills and a willingness to engage with both technical and non-technical teammates.
  • Ability to manage your own time and priorities, staying organized when working across multiple tasks or stakeholders.
  • A high level of attention to detail—especially when dealing with data that drives critical operational decisions.
  • Flexibility to travel occasionally to domestic and international partner locations (including APAC and EMEA) to support stakeholder alignment and business initiatives

Nice to have

  • Academic or project-based exposure to computer vision or machine learning concepts (e.g., coursework, capstone projects, or personal projects)
  • Experience with VBA & Macros in Excel is a plus
  • Curiosity about predictive analytics or an interest in understanding the difference between lead indicators (metrics that predict an event) and lag indicators (metrics that report what already happened)

What the JD emphasized

  • computer vision platforms
  • detection outputs
  • false positives and false negatives
  • tuning is needed
  • data pipeline health
  • container-based applications
  • Linux environments
  • change validation
  • before-and-after comparisons

Other signals

  • computer vision platforms
  • analysis and reporting
  • data-informed decisions
  • operational improvements
  • false positives and false negatives
  • tuning is needed
  • data pipeline health
  • change validation
  • before-and-after comparisons