A hands-on role for someone who wants to build analytics infrastructure on real production data and surface insights that drive business decisions.
We're a small team building two products. Skillify runs career workshops and mentorship programs for students — we've reached thousands of students across 220+ schools. Kinship Labs (kinshipcomms.ai) is the AI-powered relationship management platform that powers Skillify and helps other organizations maintain personalized, human connections at scale across SMS and email. The metrics you'll build will drive decisions across both.
You'll work directly with the engineering and product teams to build dashboards, define key metrics, and run analyses on real production data. This is hands-on work — writing SQL against production databases, designing visualizations stakeholders actually use, and making the case for what we should build next.
Work environment: full-time office setting with a small, collaborative team. You'll have a dedicated workstation, access to professional AI tooling (Claude, OpenAI, etc.), and direct mentorship from senior engineers.
What you'll actually do
Metrics dashboard development (60%)
- Design and build business intelligence dashboards for the Skillify and Kinship platforms
- Write SQL queries against PostgreSQL (Supabase) to extract engagement, pipeline, and communication metrics
- Create visualizations for KPIs: response rates, pipeline progression, message volume, user engagement
- Collaborate with stakeholders to define meaningful metrics that drive business decisions
- Build automated reporting pipelines for recurring analytics needs
Data analysis & insights (30%)
- Conduct cohort analysis to understand user behavior and retention patterns
- Analyze pipeline conversion rates and identify bottlenecks
- Design and support A/B testing frameworks for product experiments
- Build predictive models for churn risk, engagement scoring, or pipeline forecasting
- Present findings and recommendations to the team in weekly reviews
Documentation & knowledge transfer (10%)
- Document data pipelines, queries, and dashboard architecture
- Create runbooks for ongoing metrics maintenance
- Build a data dictionary for key tables and business definitions
What you'll learn
By the end of this internship, you'll be able to:
- Design data pipelines and dashboards — build end-to-end analytics from raw database queries to stakeholder-ready visualizations
- Write production SQL — query relational databases (PostgreSQL) for complex business analytics including joins, aggregations, window functions, and CTEs
- Conduct rigorous analysis — apply statistical methods to answer business questions and validate hypotheses
- Define and track KPIs — translate business goals into measurable metrics and build systems to track them
- Use modern development workflows — Git/GitHub, code review, and collaborative development practices
- Communicate technical findings — present data insights and recommendations to non-technical stakeholders
What we're looking for
Required
- Currently enrolled in or recently completed a Master's program in Data Science, Statistics, Information Science, or related field (exceptional Bachelor's candidates considered)
- Coursework in statistics, machine learning, or quantitative analysis
- Proficiency in SQL (intermediate level)
- Familiarity with Python for data analysis (pandas, numpy, matplotlib/seaborn)
- Strong written and verbal communication skills
Nice to have
- Experience with dashboard/visualization tools (Metabase, Tableau, Looker, Superset, or similar)
- Familiarity with Git and GitHub
- Experience with PostgreSQL or similar relational databases
- Exposure to dbt, Airflow, or other data pipeline tools
- Kaggle competitions, research projects, or analytics portfolio work
Tech you'll work with
- Database: PostgreSQL via Supabase (read access)
- Languages: SQL, Python, JavaScript
- Backend context: Node.js, Express, Prisma ORM
- Version control: Git/GitHub
- Communication: Slack
Mentorship & path to full-time
You'll get daily check-ins with progress review, weekly 1:1 mentorship sessions, direct access to senior engineers for technical guidance, and code review on all your work.
This internship is designed with a potential transition to a full-time role in mind. At the end of the 3-month program, strong performers may be offered a full-time position as a Data Scientist or Analytics Engineer. Final hiring depends on mutual fit, business needs, and available funding at the time of evaluation.
Sound like you?
If you read this and thought "I was literally born for this" — we want to hear from you.