When you join Verizon
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Verizon Cybersecurity (VCS) is looking for a Cybersecurity Data Scientist, you will be at the forefront of identifying and neutralizing threats using advanced data analytics. You'll collaborate with ML engineers and cybersecurity experts to develop models that predict, detect, and mitigate cyber-attacks. Your work will directly contribute to safeguarding the information and systems of our company.
What you'll be doing...
- Developing and implementing data-driven algorithms and models for threat detection, vulnerability assessment, prediction, and prevention, with a focus on ML-based anomaly detection to identify unusual patterns that may signify a cyber threat.
- Analyzing large datasets to identify patterns and anomalies indicative of cyber threats.
- Researching and implementing cutting-edge AI/ML techniques to enhance existing security solutions and develop innovative approaches to combat emerging cyber threats.
- Staying abreast of the latest developments in AI/ML and the cybersecurity landscape to identify new opportunities and mitigate potential risks.
- Contributing to the continuous improvement of our cybersecurity strategies through innovative data science approaches.
- Collaborating with the cybersecurity team to integrate advanced data science models, including anomaly detection and user behavior analytics, into the cybersecurity framework.
- Communicating complex data findings, model predictions, and the implications of user behavior analytics to non-technical stakeholders to support informed decision-making.
- Developing statistical models and algorithms to predict potential security breaches and threats based on historical and real-time data.
- Designing, developing, and implementing machine learning models to improve cybersecurity measures, including but not limited to intrusion detection systems, malware analysis, and fraud detection.
- Ensuring that all data handling and processing comply with relevant data protection regulations and ethical standards.
- Creating reports and visualizations that communicate findings and insights to non-technical stakeholders to aid in decision-making.
- Developing sophisticated prompting strategies to make the best use of 3rd-party LLMs via API.
- Exploring the latest techniques with RAG, Context Expansion, and Function Calling.
- Creating and curating large datasets and knowledge graphs from internal cybersecurity documentation.
- Fine tuning and deploying open-source LLMs to adapt them for a specific task or purpose.
- Leading and mentoring both junior and senior data scientists and data engineers.
- Developing production code and advocating for the best coding and engineering practices.
- Participating in project planning, review, and retrospective sessions.
- Participating in Agile process fulfilling the rotational duty of Scrum master.
- Collaborating with security analysts and engineers to integrate AI/ML models into security information and event management (SIEM) systems, intrusion detection systems (IDS), and other security tools.
You'll need to have: - Bachelor's degree or four or more years of work experience.
- Six or more years of relevant work experience.
- Four or more years of Data Science experience in Python-based Data Science stack: Scikit-Learn, TensorFlow, PyTorch, Pandas, NumPy, Polars, Matplotlib, Pyspark.
- Four or more years of experience in developing predictive models related to anomaly detection.
Even better if you have: - Master's or advanced degree in Data Science.
- Deep understanding of cybersecurity principles, practices, tools, and technologies.
- Understanding core cybersecurity concepts (e.g., authentication, encryption, network security).
- Knowledge of common cyber threats and attack vectors (e.g., phishing, DDoS, malware).
- Experience analyzing security-related data (e.g., log files, network traffic).
- Familiarity with security information and event management (SIEM) systems and techniques.
- Big Data Technologies, experience in managing and analyzing large-scale datasets.
- Familiarity with big data technologies and platforms (Dask, BigQuery, Kafka, Spark Structured Streaming).
- Cloud Platforms: experience with cloud platforms and services, particularly for data analytics (e.g., AWS, GCP).
- Knowledge of cloud-based security and data analytics solutions.
- Statistical Modeling experience: foundation in statistical analysis and modeling techniques.
- Proficiency in data visualization tools and libraries (e.g., Seaborn, Plotly).
- Ability to communicate insights effectively through visual representations.
- Knowledge of threat intelligence sources and frameworks (e.g., MITRE ATT&CK, STIX/TAXII).
- Experience in using threat intelligence to inform data analysis and model development.
- Experience with anomaly detection techniques and algorithms.
- Experience with security orchestration, automation, and response (SOAR) platforms (e.g. IBM Resilient).
- Experience with security information and event management (SIEM) tools (e.g., Splunk, Google Chronicle).
- Proven experience in data science, machine learning, or a related role, with a strong preference for experience in cybersecurity.
Where you'll be working In this hybrid role, you'll have a defined work location that includes work from home and a minimum eight assigned office days per month that will be set by your manager.
Scheduled Weekly Hours 40
Equal Employment Opportunity We're proud to be an equal opportunity employer - and celebrate our employees' differences, including race, color, religion, sex, sexual orientation, gender identity, national origin, age, disability, and Veteran status. At Verizon, we know that diversity makes us stronger. We are committed to a collaborative, inclusive environment that encourages authenticity and fosters a sense of belonging. We strive for everyone to feel valued, connected, and empowered to reach their potential and contribute their best. Check out our diversity and inclusion page to learn more.
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