IT Intern

Tumi • Edison, NJ

1d

About The Position

The IT Intern will work with various teams across the IT department to deliver small, well-scoped projects supporting Samsonite Group’s Retail and E-commerce analytics. This position will work with digital engineers, data engineers and analysts to build pipelines in Digital Commerce products, Snowflake, model retail datasets using SQL, and use Python Plus AI-assisted development to accelerate delivery, testing, and documentation. This person will also get hands-on opportunities to build and deploy AI agents to execute repeatable tasks at scale (with safe guardrails) and build lightweight ML models for forecasting and prediction (e.g., demand/sales forecasts, propensity indicators), starting in a sandbox and progressing to controlled production pilots.

Requirements

• Ability to work with digital engineers, data engineers and analysts.
• Knowledge of building pipelines in Digital Commerce products.
• Proficiency in Snowflake.
• Proficiency in SQL for modeling retail datasets.
• Experience with Python.
• Familiarity with AI-assisted development.
• Ability to build and deploy AI agents.
• Ability to build lightweight ML models for forecasting and prediction.

Responsibilities

• Build or enhance ingestion for common retail feeds.
• Implement Snowflake loading patterns with optional automation (if applicable).
• Create curated analytics-ready tables from raw feeds using SQL.
• Support omnichannel reporting by connecting various internal and external retail data elements.
• Implement retail-relevant data quality checks like uniqueness/deduping of customers, referential integrity (orders, product/store IDs) and anomaly detection (drops/spikes in sales, cancellations, returns).
• Build an agent to automate quality check and reporting.
• Participate in system and integration testing in various other initiatives running in the departments.
• Build a simple agent to generate validation SQL from templates and produce a report (Phase 1 — Sandbox Learning, Weeks 1–3).
• Expand agent to scheduled runs, consistent output, and alerting (Phase 2 — Controlled Pilot, Weeks 4–7).
• Add guardrails (scope-limited actions), logging/audit trail, performance controls, and human review workflows to agents (Phase 3 — Scale & Hardening, Weeks 8–12).
• Work with a mentor to design and prototype a predictive model using curated datasets.
• Define feature set (lags, rolling windows, promos, seasonality).
• Define testing approach (time-based splits).
• Perform model training and evaluation.
• Write forecast outputs back into data tables for reporting.
• Provide basic explainability summary (top drivers / feature importance).
• Deliver production-ready ingestion pipelines into Snowflake (raw → staging).
• Deliver curated retail data models (fact/dim tables).
• Deliver a data quality suite with automated checks (SQL and Python).
• Deliver at least one deployed agent that executes repeatable tasks at scale (logging and guardrails).
• Deliver one forecasting/prediction prototype with documented accuracy.
• Deliver documentation: data dictionary plus pipeline runbook plus lineage plus model card.
• Deliver a final demo showing measurable improvements (quality, timeliness, usability, or forecast accuracy).

Benefits

• Dynamic working environment
• Community where each team member is empowered with an entrepreneurial spirit
• Associates are respected as a vital part of the organization and recognized for their contributions
• Workplace that gives every individual the opportunity to make an impact
• Guidance towards individual career growth
• Professional training and development initiatives
• Motivating, exciting environment
• Competitive salaries
• Comprehensive benefits programs

What This Job Offers

• Career Level: Intern
• Education Level: No Education Listed