Data Analyst (remote, Rou)

CrowdStrike CrowdStrike · Enterprise · Romania · Remote

CrowdStrike is seeking a Data Analyst for their Generative AI Research Center. This junior/entry-level role focuses on data and corpus labeling, data gathering, cleaning, preprocessing, and exploratory data analysis to support LLMs and cybersecurity initiatives. The analyst will ensure data accuracy and consistency, create dashboards and reports, and support MLOps pipelines. Requires proficiency in Python, SQL, and data labeling tools, with a strong interest in cybersecurity.

What you'd actually do

  1. Label and annotate cybersecurity-related datasets to prepare them for analysis and machine learning tasks
  2. Ensure labeling accuracy and consistency across different datasets, including threat intelligence data, incident reports, network logs, etc.
  3. Gather data from various cybersecurity sources, including threat intelligence feeds, logs, and internal reports
  4. Clean and preprocess data to make it suitable for analysis and modeling
  5. Perform exploratory data analysis to uncover patterns, trends, and insights related to cybersecurity threats and vulnerabilities

Skills

Required

  • Bachelor's degree in Computer Science or related STEM field
  • Proficiency in data manipulation and analysis tools (e.g., Python, SQL)
  • Familiarity with relevant libraries and frameworks (e.g., TensorFlow, PyTorch)
  • Experience with data labeling and annotation tools
  • Strong analytical and problem-solving skills
  • Understanding of cybersecurity concepts
  • Excellent communication and collaboration abilities
  • Attention to detail and a commitment to data accuracy

Nice to have

  • Existing exposure to Go, AWS, Cassandra, Kafka, Elasticsearch
  • Experience with Language Models
  • Experience with Data Science
  • Experience with Data Engineering
  • Experience with data labeling and annotation tools, particularly in a cybersecurity context

What the JD emphasized

  • data and corpus labeling
  • supporting our large language models (LLMs)
  • ensuring the accuracy and quality of the data used to train models

Other signals

  • supporting our large language models (LLMs)
  • data and corpus labeling
  • enhancing our products capabilities by ensuring the accuracy and quality of the data used to train models