Fractional ML Engineer / Data Scientist

JPC Partners; Remote; USD 60,000 - 100,000; Part time

Job summary

An innovative firm is seeking a Fractional ML Engineer or Data Scientist to enhance cybersecurity efforts by analyzing real-time network traffic logs. This role involves designing machine learning workflows and extracting actionable insights from structured log data. Ideal candidates will have a strong background in data science and cybersecurity principles, with the ability to build lightweight detection models and improve existing data pipelines. Join a dynamic team to make a significant impact in the cybersecurity landscape while working on exciting projects that leverage cutting-edge technologies.

Qualifications

• 3-5+ years of experience in Data Science or Machine Learning Engineering.
• Experience with real-world datasets and production-grade ML workflows.

Responsibilities

• Explore and model Zeek and NetFlow log data.
• Build and test supervised and unsupervised models for traffic classification and anomaly detection.

Skills

Data Science

Machine Learning Engineering

Python

SQL

Cybersecurity Principles

Education

Bachelor's Degree in Computer Science or related field

Tools

Pandas

Scikit-learn

Jupyter

GCP

Docker

Job description

JPC Partners is looking for a Fractional ML Engineer or Data Scientist to help our Cybersecurity client analyze and model data from real-time network traffic logs (primarily Zeek conn.log, DNS logs, and NetFlow). Our goal is to extract actionable insights and build lightweight detection models for anomalous behavior, segmentation policy validation, and traffic classification.
You’ll be working with structured log data and should be comfortable designing and evaluating machine learning workflows that can scale or be embedded into lightweight data pipelines (e.g., Jupyter, Python, cloud-ready).
This is part-time/project-based, ideal for someone with a cybersecurity lens and ML fluency.

Responsibilities

• Explore and model Zeek and/or NetFlow log data
• Help improve existing pipeline logic (cleaning, enrichment, labeling)
• Build and test supervised and unsupervised models for:
  • Traffic classification (e.g., system personality or app type)
  • Anomaly detection (e.g., port scanning, lateral movement)
  • Baseline behavior for network segmentation enforcement
• Optionally develop output for visualization or SIEM dashboards

Example Use Cases

• Classify device types based on observed connection patterns
• Detect rogue internal services using legacy or high-risk ports
• Map internal east-west traffic to segmentation policy gaps
• Identify abnormal DNS behavior and data exfiltration attempts

Required Skills

• 3 – 5+ years of professional experience in Data Science, Machine Learning Engineering, or a related field
• Demonstrated experience working with real-world datasets, model deployment, and production-grade ML workflows

Desired Skills

• Python (Pandas, Scikit-learn, Jupyter), some SQL
• Experience with GCP and implementing cloud-based ML systems
• Experience with Zeek, NetFlow, or PCAP-derived data
• Familiarity with cybersecurity principles (MITRE ATT&CK, segmentation, IDS logic)
• Bonus: TensorFlow/PyTorch, Docker, experience integrating with SIEMs or cloud logging platforms
• Experience with implementing LLMs in cloud production environments