AI Data Science Internship/Residency Program — Las Vegas

Internship Job Description — Kinship Labs

Position Title: Data Science Intern

Company: Kinship Labs

Location: Las Vegas, NV (In-Person)

Duration: 3 months

Hours: Full-time, Monday–Friday, 9:00 AM – 5:00 PM (40 hours/week)

Compensation: Paid Monthly Stipend

Level: Graduate (Master's preferred) or exceptional Bachelor's candidates

Company Overview

Kinship Labs builds AI-powered relationship management software that helps organizations maintain personalized, human connections at scale. Our platform automates multi-channel communication (SMS, Email) while preserving authentic engagement through intelligent AI agents. We serve education, healthcare, and customer success teams managing hundreds to thousands of relationships.

Position Description

The Data Science Intern will work directly with the engineering and product teams to build analytics infrastructure, define key metrics, and surface insights that drive business decisions. This is a hands-on role involving real production data, customer analytics, and direct impact on how we measure success.

Work Environment: Full-time office setting with a small, collaborative team. The intern will have a dedicated workstation, access to professional AI tooling (Claude, OpenAI, etc.), and direct mentorship from senior engineers.

Primary Responsibilities

• 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 key performance indicators (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