Deliver fast, trustworthy analytics for Eskwelabs by building dashboards, conducting deep‑dive analyses, and designing reusable report templates that AI agents can use for automated reporting. Combine existing data infrastructure (datasets, metrics definitions, report templates, AI prompts) to create new deliverables—or expand the infrastructure with standardized queries, components, and schemas.

Your work will contribute to the following outcomes:
‍1. Clear, reliable dashboards that help teams make decisions quickly and consistently.
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2. Insightful deep‑dive reports that explain drivers, trends, and opportunities, improving strategy and execution.

3. Reusable, AI‑ready report templates that standardize metrics, prompts, and visuals for automated generation.

4. Stronger data culture: consistent definitions, better documentation, and reproducible workflows across teams.

A. Dashboard Creation
Build user‑friendly dashboards from provided data based on requested tools. Apply consistent metrics and visual standards; include filters, tooltips, and clear labeling.

‍Example: A cohort performance dashboard (enrollments, completion, NPS proxies, placement) with weekly refresh instructions and a short usage guide.
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‍B. Deep Dive Research, Analysis & Reporting
Investigate a focused question (e.g., campaign performance, learner retention, pricing) using exploratory analysis and clear storytelling and prepare a final report, which might can leverage existing templates & AI workflows. Document assumptions, methods, and limitations.

‍Example: A 4–6 page analysis of a marketing funnel with cohort breakdowns, drivers of drop‑off, and recommendations.

C. Report Templates & AI Conversational Dashboards
Create report shells that AI agents can populate: defined sections, metric blocks, AI prompts, SQL/query slots, chart specs, etc. Provide a data dictionary and guardrails.

‍Example: A monthly program health template with pre‑wired KPIs, chart placeholders, and prompts for auto‑generated commentary.

General Description
This is a task‑based, production‑oriented analytics role. Work arrives via tickets with scope, priority, and due dates. Contributors ship accurate, interpretable dashboards and analyses and, when feasible, convert one‑offs into reusable templates, metric definitions, and query components. AI tools are used for code suggestions, draft narratives, and QA checks—paired with human verification, documentation, and data ethics. Success is measured by speed, accuracy, clarity, reuse, and stakeholder satisfaction.

This track is ideal for anyone who enjoys translating data into clear decisions and stories. If you’re exploring Eskwelabs Innovation Fellows (EIF) options and love structuring messy information, this is a strong fit.

You might come from backgrounds such as:
- Data & Tech: statistics, computer science, information systems, data analytics.
- Business & Economics: management, economics, finance, marketing analytics.
- Social Science & Education: psychology, public policy, education research with a quantitative bent.

What matters most is your attitude and curiosity. You care about accuracy, clarity, and reproducibility; you like making complex things understandable; and you’re open to learning AI‑assisted analysis workflows.

- Data cleaning and transformation (spreadsheets/SQL/Python or R)
- Visualization and dashboard design (clarity, hierarchy, accessibility)
- Metrics design and documentation (definitions, caveats)
- Analytical thinking (hypotheses, causality cautions, A/B basics)
- Communicating insights with short executive summaries
- Using AI to accelerate analysis, code review, and report drafting

Traits
- Bias for action; iterates quickly with versioned improvements.
- Systems thinking; turns ad‑hoc analyses into reusable templates/components.
- High bar for clarity and accuracy; documents sources and assumptions.
- Data ethics and provenance discipline; respects privacy and licensing.

‍Skills
- Data & Code: spreadsheets, SQL (select/joins/windows), and/or Python/R.
- Viz & Dashboards: chart choice, labeling, filters, accessibility.
- Analysis: cohorting, segmentation, trend analysis, simple forecasting.
- AI usage: prompt craft for analysis/writing, code linting, test generation.

- Data Analyst / BI Developer: builds robust dashboards and data models.

- Analytics Engineer: metrics layers, transformations, documentation.

- Data Product / Operations Analyst: process analytics, experimentation, enablement.

- Research & Insights / Evaluation: program impact, outcomes measurement.

All tasks are listed in a shared spreadsheet with task title, type, deadline, deliverable format, and submission contact. Analysts check the sheet to see available tasks. To claim a task, add your name in Assigned To and set status to In Progress. Once claimed, you are responsible for delivering by the deadline.

Work independently but communicate proactively. If scope or data requirements are unclear, message the requester or Analytics Manager. Submit deliverables per the sheet instructions (e.g., dashboard link, notebook, SQL/script file, and a short executive summary). Mark Submitted when complete; the approver updates to Approved or Revisions Needed.

You will report to the Data and Decision Systems lead of Eskwelabs. They meet with analytics contributors twice a month to review progress, share feedback, and demo new methods—especially how to structure AI‑ready templates, document metrics, and set up reproducible environments.

You will be judged based on the quality and speed of your deliverables. Strong performance also includes: correct and reproducible results, clear documentation (sources, methods, caveats), stakeholder‑fit dashboards, and reusable components/templates that improve the next analyst’s speed.