AI/ML Analytics Engineer Intern - Advanced Track (Summer/Fall 2026)

## About the Role Our growth engine combines Smartlead for outbound, Brevo for marketing automation, Firebase/Mixpanel for product analytics, Stripe for billing, and AI bots on our website. We believe AI and ML will drive much of what we do — from campaign optimization to churn prediction to automated daily business briefs. We're looking for a strong, technically capable analytics intern who can help us build the foundation of our data + AI stack and directly shape how we make decisions as a business. ## Why Join Us - 🚀 Impact: You'll build pipelines, dashboards, and ML models that directly guide product and growth. - 👥 Mentorship: Work alongside senior engineers and founders who have built and scaled SaaS products. - 🌱 Career Track: Designed as a trial-to-hire pipeline for permanent roles in 2026. - 💡 Full Startup Exposure: Be involved in both the technical build and the strategic conversations behind it. ## What You'll Do - Unify data sources (Smartlead, Brevo, Firebase, Stripe, bots, CRM) into BigQuery. - Design and maintain dbt models (fact/dimension tables, KPI marts). - Build dashboards and weekly briefs in Looker Studio/Notion that track KPIs and anomalies. - Deploy ML models (forecasting, churn, LTV, anomaly detection) using BigQuery ML / Vertex AI. - Prototype AI agents that generate narrative business summaries from data. - Audit and improve our current stack: identify missing events, attribution gaps, and data quality issues. - Document schemas, definitions, and playbooks for internal use. ## What You'll Bring - 2–3 years of relevant coursework, projects, or internship experience in analytics, data engineering, or ML. - Strong SQL skills and familiarity with dbt or equivalent transformation frameworks. - Experience working with a data warehouse (BigQuery, Snowflake, Redshift). - Hands-on exposure to Python for data analysis and/or ML (scikit-learn, TensorFlow, PyTorch, or BigQuery ML). - Ability to connect technical work → business impact. - Comfort working in a fast-paced, ambiguous environment with multiple priorities. ## What You'll Learn & Build On - End-to-end data engineering + analytics engineering workflow in a modern SaaS startup. - Real-world ML deployment (forecasting, churn, anomaly detection). - How to use AI agents/LLMs to automate reporting and insight generation. - How data informs product decisions, growth strategy, and revenue expansion. ## Nice to Haves - Prior startup experience or a "first data hire" mindset. - Experience integrating SaaS tools (marketing automation, outbound, billing). - Familiarity with MLOps (model retraining, monitoring, drift detection). - Portfolio of data/ML projects (GitHub, Kaggle, personal site). ## Candidate Screening Questions 1. Data Integration - 👉 We use Smartlead for outbound, Brevo for marketing, Firebase for product events, and Stripe for billing. Sketch how you'd ingest these into BigQuery. Which tables would you build first? 2. SQL/dbt Modeling - 👉 Write a sample SQL query (or describe the logic) to calculate weekly active users (WAU) from a fact_events table with columns: user_id, event_name, event_timestamp. 3. ML Forecasting - 👉 Our MRR is ~$20k/month and fluctuates week to week. How would you forecast the next quarter's MRR using BigQuery ML or Python? What assumptions would you check? 4. Anomaly Detection - 👉 If our email reply rate drops -15% overnight, how would you detect this automatically and confirm it's not just noise? 5. Gap Analysis & Business Impact - 👉 Imagine you join and see Smartlead + Brevo integrated but no connection to Stripe revenue. How would you audit our tracking and explain what's missing?