Brief Job Description:

Owns the end-to-end data analytics lifecycle, from defining measurement frameworks and surfacing insights to supporting product and model decisions. The role works closely with engineering, product, and ML teams, builds and maintains data pipelines and dashboards, and helps shape the analytical foundation of the AI software development project. Requires 3+ years of data analytics experience, with a track record of influencing product outcomes in a fast-moving technical environment.

Mission:

Turn raw product, behavioural, and model data into clear, trustworthy insight that helps the team make better decisions, faster.

Education:

• Bachelor’s degree or higher in Statistics, Computer Science, Engineering, Economics, or a related quantitative field — or equivalent practical experience.

Responsibilities:

• Define measurement frameworks and metrics with product and engineering teams.
• Build and maintain data pipelines, dashboards, and reports.
• Analyse product, behavioural, and model data to surface actionable insights.
• Support experiment design and A/B test analysis.
• Ensure data quality and consistency across sources.

Qualifications & Experience:

• 3+ years’ experience in data analytics or a similar quantitative role.
• Strong SQL and proficiency in Python or R for analysis.
• Experience building dashboards (e.g. Tableau, Power BI, Looker, or similar).
• Solid grasp of statistics and experiment design.
• Clear communication of findings to technical and non-technical audiences.

Nice to Have (Not Obligatory):

• Experience with cloud data warehouses (BigQuery, Snowflake, Redshift).
• Familiarity with ML workflows and model evaluation metrics.
• Familiarity with GitLab for versioning analysis code and pipelines.
• Exposure to EdTech or AI products.