Senior Cyber Security Data Scientist

When you join Verizon

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Verizon is one of the world's leading providers of technology and communications services, transforming the way we connect around the world.

We're a human network that reaches across the globe and works behind the scenes. We anticipate, lead, and believe that listening is where learning begins.

In crisis and in celebration, we come together-lifting up our communities and striving to make an impact to move the world forward.

If you're fueled by purpose, and powered by persistence, explore a career with us. Here, you'll discover the rigor it takes to make a difference and the fulfillment that comes with living the #NetworkLife.

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.
• Analyzing large datasets to identify patterns and anomalies indicative of cyber threats, employing user behavior analytics to understand normal and suspicious user activities.

Perform analysis to uncover hidden patterns, correlations, and insights that could inform cybersecurity strategies and threat detection.

• 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.
• Contributing to the continuous improvement of our cybersecurity strategies through innovative data science approaches, staying abreast of the latest in machine learning and cybersecurity trends.
• 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, user behavior anomaly detection.
• Working with AI / ML engineers and other cybersecurity team members to provide data that supports model building and refinement.
• 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.
• Keeping abreast of the latest trends and developments in data science and cybersecurity to improve skills and knowledge.
• 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 advocate 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
• Collaborateing 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 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. Experience in applying statistical methods to develop sophisticated models to process and analyze large volumes of data for anomaly detection, hypothesis testing, regression, clustering, and classification.
• 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.
• Ability to develop models to identify unusual or malicious behavior in network traffic or system logs.
• Experience with security orchestration, automation, and response (SOAR) platforms (e.g. IBM Resilient).
• Ability to develop automated workflows for threat detection and incident response.
• Experience with security information and event management (SIEM) tools (e.g., Splunk, Google Chronicle).
• Knowledge of log management, parsing, and analysis.
• Proven experience in data science, machine learning, or a related role, with a strong preference for experience in cybersecurity.
• Knowledge of SIEM tools and platforms that support user behavior analytics and anomaly detection. Demonstrable understanding of their architecture and data integration points.
• Demonstrated experience in designing and implementing scalable AI models for production, to detect, analyze, and respond to advanced cyber threats in real-time.
• Deep technical understanding of Machine Learning, Deep Learning architectures like Transformers, training methods, and optimizers.

Autoencoders, ML-Ops processes

• Experience building scalable applications with LLMs, using frameworks such as LangChain.
• Certifications such as Certified Information Systems Security Professional (CISSP), Certified Data Privacy Solutions Engineer (CDPSE), GIAC Machine Learning Engineer (GMLE) are highly desirable.
• Experience with cloud security tools across platforms like AWS, Azure, or Google Cloud.
• Strong understanding of cybersecurity principles, threats, and countermeasures, with a focus on analyzing user behavior to detect potential insider threats or compromised accounts.
• Background in threat modeling, risk assessment, and digital forensics.
• Critical Thinking : Analytical and critical thinking skills, with the ability to draw insights from complex data sets and inform decision-making.
• Ability to communicate complex data in a clear and understandable manner.
• Proven ability to collaborate within and across teams.
• Excellent written and verbal communication skills.
• Strong problem-Solving and Analytical Skills, with the ability to address complex cybersecurity challenges using data-driven approaches.

Experience in identifying and mitigating technical and business risks.

• Generative AI experience :
• Experience with Large Language Models ecosystem, prompt engineering, Langchain framework.
• Experience working with Google Vertex AI.
• ML OPS experience.
• Vector databases familiarity, ChromaDB, pgvector.
• Graph databases, Neo4j, Cypher query language.

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

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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