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Junior Data Analyst

Posted on March 11, 2025

  • Full Time

Junior Data Analyst

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About the Role:
Support data analysis tasks that are both ad-hoc /as-needed to support better data-driven business and product decisions
Scope is initially around AI/ML risk products, then for SLG/Mobile (data gaps) then larger P&L
Must work closely with stakeholders across multiple time zones (need person to be flexible)

What you'll do:

    • Produce key metrics, dashboards, and reports to support PM asks (AI/ML initiatives, tech partnerships etc.)
    • Present data findings and insights to internal stakeholders, translating complex data into actionable recommendations.
    • Develop and maintain interactive dashboards for real-time analytics and reporting.
    • Collaborate with cross-functional teams in UK, NZ, India and US time zones, ensuring timely updates and support for all teams.
    • Write, optimize, and troubleshoot SQL queries to access, manage, and analyze data within Snowflake or other relational databases.
    • Assist in machine learning projects by preparing data and evaluating models, applying metrics like precision, recall, and accuracy.
    • Build and maintain a comprehensive data dictionary to standardize and improve data usage across the organization.
    • Support data quality initiatives by troubleshooting and validating data integrity in reports and dashboards.

What you'll bring:

    • Education: Bachelor’s degree in Data Science, Statistics, Computer Science, or a related field, or equivalent work experience.
    • Some Proficiency in SQL for data querying and analysis.
    • Familiarity with relational database concepts and data structures.
    • Strong understanding of statistical fundamentals and measures.
    • Knowledge of data warehousing principles and ETL processes.
    • Experience with data visualization tools, preferably Tableau.
    • Experience with data warehouse tools, preferably Snowflake.

Preferred Qualifications:

    • Basic knowledge of Python for data manipulation and automation
    • Prior exposure to machine learning workflows or data preparation for modeling.
    • Foundational knowledge of machine learning concepts and metrics, such as precision, recall, and accuracy
    • Ability to communicate effectively with technical and non-technical team members.
    • Flexibility to work across multiple time zones (UK, US, India, NZ).

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