Data Science Intern, Summer 2026

Crusoe•San Francisco, CA

22h

About The Position

As a Data Science Intern at Crusoe, you will tackle the challenge of turning massive volumes of raw syslog data into actionable intelligence. Crusoe’s infrastructure powers the next generation of climate-aligned computing, and ensuring the reliability of our network is paramount. This role is a blend of systems engineering and applied machine learning; you won’t just be building models in a vacuum—ingesting, normalizing, and extracting signal from real-world network noise to protect our operations and enhance security. Your work will directly impact our ability to scale sustainably by automating the detection of operational and reliability issues that traditional rule-based systems miss. You will own the end-to-end lifecycle of a lightweight, learning-based system, from data pipeline design to prototype visualization. We are looking for an innovative, hands-on student who is excited by unstructured data and eager to build practical solutions for complex infrastructure. This is a full-time internship designed for an aspiring engineer who values practical system design over academic theory.

Requirements

Educational Foundation: Currently pursuing a BS, MS, or PhD in Computer Science, Data Science, Electrical Engineering, or a related technical field graduating between Fall 2026 and Spring 2027.
Python Proficiency: Strong Python programming skills with the ability to write clean, maintainable, and efficient code.
Data Dexterity: A high level of comfort working with large, unstructured datasets and the "messiness" of real-world logs.
Analytical Mindset: A solid foundational understanding of statistics and data analysis techniques.
Systems Interest: A genuine curiosity about computer networking, infrastructure, and the art of observability.
Problem Solving: The ability to move from an abstract problem to a functional prototype through logical deduction and experimentation.

Nice To Haves

Infrastructure Knowledge: Previous familiarity with syslogs, infrastructure monitoring, or large-scale distributed systems.
Data Science Toolkit: Hands-on experience using libraries such as pandas and scikit-learn for data manipulation and modeling.
Advanced ML Techniques: Prior exposure to clustering algorithms or specific anomaly detection methodologies.
Networking Fundamentals: Basic understanding of networking hardware, including the functions of routers, switches, and their associated logs.
UX/UI Exposure: Experience building basic dashboards or using visualization tools to represent complex data trends.

Responsibilities

Data Pipeline Engineering: Design and implement a robust pipeline to ingest and preprocess large volumes of syslog data from diverse network devices.
Log Normalization: Develop methods to group and normalize unstructured log messages without relying on rigid, predefined formats.
Baseline Modeling: Build statistical or ML-based baselines to define "normal" behavior patterns across our network environment.
Anomaly Detection: Implement mechanisms to detect irregularities across multiple time scales, including sudden bursts, sustained shifts, and rare event occurrences.
Prototyping & Visualization: Create a functional alerting or visualization mechanism to surface critical findings to the broader engineering team.
Signal Optimization: Evaluate signal quality through iterative testing to reduce false positives and ensure alerts are meaningful and actionable.
Technical Documentation: Document your assumptions, architectural tradeoffs, and system limitations to facilitate future production integration.
Cross-Functional Collaboration: Partner with network and systems engineers to understand device roles and refine the relevance of detected anomalies.