Data & Analytics Intern

Week 1: findlucid data landscape and privacy rules

• Learn what data findlucid generates: signups, activation, retention, memberships, and support tickets.
• Study the privacy rules: aggregate data only, no identifiable member data beyond your assignment, no exports.
• Meet your mentor, agree on your weekly schedule, and write your personal learning plan.
• Read the intern handbook and the data-handling rules that apply to your role.

Week 2: SQL fundamentals

• Work through a SQL curriculum: selects, joins, aggregations, window functions.
• Practice on a sample dataset modeled on findlucid's schema, never on real member data.
• Exercise: answer five business questions with queries and check your logic with your mentor.
• Learn to document what a query does so someone else can trust it.

Week 3: Metrics and definitions

• Study findlucid's core metrics: what signup, activation, and retention mean here, and why definitions matter.
• Learn how the same metric can lie when the definition is sloppy.
• Exercise: write metric definitions for five findlucid metrics and have them reviewed.
• Shadow a metrics review with the founder as an observer.

Week 4: Dashboards and storytelling

• Learn dashboarding basics: one screen, one question, no clutter.
• Study how to write findings for non-technical readers.
• Exercise: build a practice dashboard on the sample dataset.
• Phase 1 checkpoint review with your mentor and updated learning plan.

Month 2: Guided analysis exercises

• Analyze sample datasets with your mentor: signup trends, activation funnels, retention cohorts.
• Every analysis is reviewed for correct method and honest interpretation.
• Practice writing one-page findings memos for a non-technical reader.
• Weekly mentor topics: correlation vs. causation, cohort thinking, and honest charts.

Month 3: Experimentation basics

• Study A/B testing: hypotheses, sample size, and why most tests are inconclusive.
• Practice analyzing a sample experiment and writing up what it actually showed.
• Study real findlucid metrics as case studies with the founder: what the numbers meant and what changed.
• First portfolio piece: a complete analysis write-up with charts, method, and honest conclusions.

Retention deep dive (Months 4 to 6)

Study member retention patterns from sample data: build cohort analyses and write up what drives people to stay or leave, as a practice exercise.

• Cohort analysis with charts and documented method.
• Written findings memo with recommendations framed as learning, not decisions.

Support analytics study (Months 7 to 9)

Analyze aggregate support ticket patterns from sample data: categories, volumes, and response-time trends, and draft reporting a support lead could use.

• Support analytics dashboard on sample data.
• Monthly-style report with trends and open questions.

Experiment design project (Months 10 to 11)

Design a complete experiment plan for a hypothetical product change: hypothesis, metrics, sample size, and analysis plan, reviewed for rigor.

• Experiment design document with analysis plan.
• Presentation of the design to your mentor.

Capstone (month 12)

Month 12: present your analyses, dashboard, and experiment design to the founder; discuss how your thinking about data matured and where honest analytics can mislead.