Data Analyst

We are looking for a Data Analyst to turn the data generated across our learning, assessment, and hiring products into decisions for our internal product and business teams, and for the insights we surface to universities and enterprise clients. You will partner closely with Product, Engineering, Data Science, Customer Success, and Sales to define metrics, build self-serve and client-facing dashboards, and run analyses that shape roadmap, pricing, and customer outcomes. This is a high-visibility role that sits at the intersection of product analytics and customer-facing insight delivery.

Responsibilities:

Own end-to-end analysis of platform data across Neo PAT, Neo Exam, Neo Colab, Neo Coder, and Neo Hire, including assessment performance, coding proficiency, proctoring/integrity signals, learner engagement, and placement/hiring funnel outcomes.

Design and maintain dashboards and reports (e. g., in Power BI, Tableau, or Looker) for internal stakeholders and for client-facing deliverables used by university placement cells and enterprise L& D/HR teams.

Define, track, and report on core product and business KPIs: learner engagement, assessment completion and pass rates, skill-gap trends, cohort performance, placement conversion, time-to-hire, and platform adoption/retention.

Partner with Product Managers to evaluate feature performance, run A/B tests and cohort analyses, and translate findings into actionable roadmap recommendations.

Work with Data Engineering to define data models, ensure data quality/pipeline reliability, and improve the accessibility of learning and assessment data warehoused across products.

Support Customer Success and Sales with data-driven narrative placement outcome reports, ROI analyses, and benchmarking for university and enterprise renewals and pitches.

Collaborate with the Data Science/ML team on inputs for adaptive assessment, skill-gap prediction, and recommendation models, including feature analysis and model performance monitoring.

Conduct deep-dive analyses on anomalies (e. g., proctoring flags, unusual scoring patterns, drop-offs) and proactively surface risks or opportunities to stakeholders.

Mentor junior analysts, establish analytics best practices, and help build a scalable, self-serve data culture across the organisation.

Requirements:

Bachelor's or Master's degree in Statistics, Computer Science, Mathematics, Economics, Engineering, or a related quantitative field.

3-5 years of experience as a Data Analyst / Data Analyst, ideally in EdTech, SaaS, HR-tech, or a data-rich B2B/B2B2C product environment.

Strong SQL skills and hands-on experience querying large, complex datasets from production and warehouse systems.

Proficiency in a BI/visualization tool (Power BI, Tableau, or Looker) and comfort building dashboards for both technical and non-technical audiences.

Working knowledge of Python or R for data manipulation, statistical analysis, and automation of recurring reports.

Solid grounding in statistics, hypothesis testing, experimentation/A-B testing, and cohort and funnel analysis.

Experience translating ambiguous business questions from cross-functional stakeholders into structured analyses and clear, decision-ready recommendations.

Excellent communication skills, with the ability to present findings to both internal leadership and external clients (university TPOs, enterprise HR/L& D leaders).

Preferred Qualifications:

Prior experience with assessment, LMS, HR-tech, or recruitment-platform data (e. g., test scores, proctoring logs, ATS/hiring funnel data).

Familiarity with data warehousing and modelling tools (e. g., BigQuery, Snowflake, Redshift, dbt).

Exposure to product analytics tools (e. g., Mixpanel, Amplitude, GA4) and event-tracking instrumentation.

Experience working with or supporting Data Science teams on ML feature pipelines or model evaluation.

Prior experience in a B2B2C SaaS company serving both institutional (university) and enterprise customers.

What Success Looks Like in the First 6 Months:

Fluent in iamneo's product suite and the key data entities behind assessments, learning paths, and hiring outcomes.

Owns a core set of dashboards used weekly by Product, Customer Success, and leadership.

Has driven at least one measurable improvement to a product or business metric through analysis-led recommendations.

Recognised cross-functionally as the go-to person for questions about learner, assessment, or placement data.