Data Scientist In Cybersecurity Resume Example
Professional ATS-optimized resume template for Data Scientist In Cybersecurity positions
John Doe
Data Scientist | Cybersecurity
Email: johndoe@email.com | Phone: (555) 123-4567 | LinkedIn: linkedin.com/in/johndoe | GitHub: github.com/johndoe
PROFESSIONAL SUMMARY
Innovative Data Scientist specialized in cybersecurity with over 6 years of experience analyzing complex threat data, developing predictive models, and enhancing security protocols. Skilled in leveraging machine learning, anomaly detection, and threat intelligence to proactively identify vulnerabilities and bolster organizational resilience. Adept at translating technical findings into strategic insights for cross-functional teams in high-stakes environments. Passionate about advancing cybersecurity resilience through data-driven decision-making and cutting-edge AI applications.
SKILLS
Hard Skills
- Machine Learning & Deep Learning (TensorFlow, PyTorch)
- Anomaly Detection & Behavioral Analytics
- Network Traffic Analysis & Intrusion Detection
- Cyber Threat Intelligence & SIEM Integration
- Data Engineering & ETL Pipelines (Apache Spark, Airflow)
- Programming Languages: Python, R, SQL
- Data Visualization (Tableau, Power BI)
- Cloud Platforms: AWS, Azure Security Center
- Cryptography & Security Protocols
Soft Skills
- Critical Thinking & Problem Solving
- Cross-Functional Communication
- Ethical Hacking & Risk Assessment
- Agile & DevOps Methodologies
- Continuous Learning & Innovation
WORK EXPERIENCE
*Senior Cybersecurity Data Scientist*
*CyberShield Inc., San Francisco, CA*
June 2022 – Present
- Developed machine learning models to detect and classify emerging malware variants with 92% accuracy, reducing incident response times by 30%.
- Led the implementation of anomaly detection systems leveraging unsupervised learning on network traffic, resulting in early identification of zero-day exploits.
- Collaborated with security engineers to optimize SIEM rule sets, decreasing false positives by 25%.
- Conducted threat intelligence analysis integrating fed-enabled APIs, enhancing proactive defense strategies.
*Data Scientist – Cybersecurity Analytics*
*SecureNet Solutions, New York, NY*
August 2019 – May 2022
- Built predictive models to identify compromised accounts, achieving 88% precision and reducing credential-related breaches by 40%.
- Engineered ETL workflows for integrating logs from firewalls, IDS, and endpoint devices into centralized data lakes.
- Developed dashboards highlighting anomaly trends and threat vectors, presented findings to executive leadership.
- Supported penetration testing teams by analyzing network behavior patterns and developing custom anomaly detectors.
*Junior Data Analyst – Threat Monitoring*
*InfoSec Global, Boston, MA*
July 2017 – July 2019
- Monitored network and endpoint logs to identify suspicious activity, supporting rapid incident escalation.
- Automated report generation and data collection processes, saving 15 hours per week.
- Assisted senior analysts in developing initial anomaly detection algorithms utilizing Python scikit-learn.
EDUCATION
**Master of Science in Data Science**
Massachusetts Institute of Technology (MIT), Cambridge, MA
*2015 – 2017*
**Bachelor of Science in Computer Science**
University of California, Berkeley, CA
*2011 – 2015*
CERTIFICATIONS
- Certified Cybersecurity Analyst (CySA+) – CompTIA, 2023
- Machine Learning Specialization – Coursera (Stanford University), 2022
- AWS Certified Security – Specialty, 2024
PROJECTS
Threat Prediction Model
- Designed a multi-layer neural network to classify phishing attacks based on email metadata and content patterns, achieving 94% precision on real-world datasets.
- Integrated model into SIEM workflows for automated alerting, reducing false positives by 20%.
Network Traffic Anomaly Detection System
- Developed an unsupervised learning system using autoencoders to monitor real-time network traffic, flagging anomalous patterns indicative of intrusions without prior labeled data.
- Deployed on AWS, enabling scalable monitoring for enterprise-level networks.
Data-Driven Phishing Campaign Analysis
- Analyzed email campaign data to identify common features correlating with successful phishing attacks.
- Published insights contributed to organizational awareness campaigns, decreasing successful phishing attempts by 15%.
LANGUAGES
- English (Native)
- Spanish (Fluent)
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