About the position
The Cyber Data Science & Engineering Manager role requires leading a team in developing and implementing machine learning models for cyber threat management, demanding strong technical expertise in data science, programming, and cybersecurity. Candidates must be prepared to blend strategic leadership with hands-on technical contributions, driving both the delivery of data solutions and the team's alignment with organizational cybersecurity goals. This role directly enhances the business' ability to proactively identify and neutralize cyber threats through advanced data science techniques. By leading the development of sophisticated machine learning models and automated response systems, this role significantly reduces the time it takes to detect and react to security incidents. Furthermore, the role ensures the business benefits from the latest threat intelligence, continually adapting its defenses against emerging cyber risks. Ultimately, this leadership position strengthens the company's overall cybersecurity posture, protecting critical assets and maintaining customer trust.
Responsibilities
- Oversee the design, development, and implementation of machine learning models for detecting and responding to cyber threats across various data sources.
- Continuously refine and enhance detection algorithms based on new threat intelligence and feedback from incident response teams.
- Oversee the analysis and application of threat intelligence data to improve detection capabilities.
- Collaborate with threat intelligence teams to integrate contextual data into services to all stakeholders.
- Oversee the development of automated response systems that leverage machine learning outputs to initiate immediate actions against detected threats.
- Perform in-depth data analysis to uncover insights and trends related to cybersecurity incidents.
- Present findings and actionable recommendations to stakeholders, including technical teams and executive leadership.
- Collaborate with cross-functional teams to ensure alignment on detection strategies and response protocols.
- Contribute to the development of best practices and documentation related to machine learning and data science applications in cybersecurity.
- Drive architectural planning and deployment of data lake technology to facilitate the development of Cyber Data Science and Engineering products.
- Establish the direction of CDSE in coordination with Director+ leadership on the applications of data science to PayPal's Cyber Threat Management strategic goals.
Requirements
- Bachelor's or master's degree in computer science, Data Science, Cybersecurity, or a related field with 8+ years of experience with at least 3 years of experience leading engineering teams.
- 3+ years of experience in data science and machine learning, preferably within a cybersecurity context.
- Proficiency in programming languages such as Python or R, and experience with machine learning frameworks (e.g., MLlib, TensorFlow, PyTorch, Scikit-learn).
- Experience with data visualization tools (e.g., Tableau, Power BI, Looker).
- Experience designing new and working with existing deep learning models.
- Experience with NLP models like embedding models and transformer models.
- Experience working with time series data.
- Experience working with supervised, unsupervised, and semi-supervised machine learning settings.
- Experience working with data lake technology like Big Query and Delta Lake.
- Experience with data architecture, cataloging, and governance.
Nice-to-haves
- Experience correlating business strategic direction to a technical strategy to inform a team's technical priorities.
- Experience setting strategic direction for analytical project in the cyber domain.
- Experience leading a multinational and multi-time zone engineering team.
- Experience designing, implementing, integrating, and testing cross team software initiatives.
- Experience architecting systems and software solutions in support of business priorities.
- Familiarity with cybersecurity concepts, threat detection methodologies, and threat intelligence frameworks.
- Strong analytical skills with the ability to translate complex data into actionable insights.
- Excellent communication skills, capable of conveying technical concepts to non-technical audiences.
- Excellent written and verbal technical communication skills, capability of communicating detailed machine learning concepts to data scientists and development engineers.
Benefits
- Flexible work environment
- Employee shares options
- Health and life insurance
