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| Desired Industry: Information Technology |
SpiderID: 84855 |
| Desired Job Location: Dallas, Texas |
Date Posted: 2/3/2025 |
| Type of Position: Full-Time Permanent |
Availability Date: 02/15/2025 |
| Desired Wage: 120000 |
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U.S. Work Authorization: Yes |
| Job Level: Experienced with over 2 years experience |
Willing to Travel: Yes, 25-50% |
| Highest Degree Attained: Masters |
Willing to Relocate: Undecided |
Objective:
Experienced technical product manager specializing in SaaS analytics and machine learning (AI) with a strong background in cloud architecture, including Microsoft Azure, and AWS. Proven leadership in managing product lifecycles, with exceptional communication skills and a commitment to continuous professional development.
Experience:
PROFESSIONAL EXPERIENCE
OpenText - Cybersecurity
Data Science and AI Product Manager
May 2021 – July 2024
• Designed and led the development of a next-generation intelligence platform that unified AI-driven analytics, business intelligence, and real-time data processing to improve decision-making across security, compliance, and customer insights.
• Built a machine learning-powered analytics engine that processed structured and unstructured data from emails, logs, cloud applications, and customer interactions to detect anomalies and predict risks.
• Enhanced AI/ML threat detection accuracy (96% → 98%) while also applying the same methodologies to business intelligence use cases, such as identifying customer churn risks and product engagement trends.
• Developed an event correlation system using graph-based relationships, enabling predictive analytics for security, fraud prevention, and operational intelligence.
• Led execution across engineering, AI/ML, and product teams, improving cross-functional collaboration and streamlining workflows, increasing operational efficiency by 10%.
• Integrated real-time data streams from enterprise SaaS platforms, security systems, and behavioral analytics tools, applying AI to identify trends, optimize user experiences, and automate decision-making
Zix|AppRiver
Product Manager, Email Threat Protection and Compliance
November 2015 – May 2021
• Spearheaded initiatives to enhance cybersecurity product features, directly addressing customer feedback and KPIs to improve satisfaction and retention rates.
• Secured a patent for a machine learning-based email filtering system, enhancing compliance and raising the standard for advanced threat protection.
• Collaborated with customers and stakeholders to prioritize ML-driven feedback features, enhancing product performance by 15%.
• Delivered data protection solutions ensuring GDPR, CCPA, and HIPAA compliance, improving data risk and governance for customers.
• Partnered with MSP/MSSP stakeholders to increase adoption and upselling.
Senior Research Engineer
March 2014 – November 2015
• Researched and evaluated the viability of new regulatory email content filters for HIPAA and other compliance standards to determine their return on investment (ROI) for our email gateway product.
• Produced comprehensive dashboard reports in PowerBI to analyze filtering errors by vertical, location, and time, providing data-driven insights for feature prioritization.
• Retained an enterprise-level customer by devising and implementing a 6-week troubleshooting plan, leading to a 3-year contract extension worth $140k.
• Acted as a technical subject matter expert on the regulatory content scanning engine, ensuring strategic alignment with customer compliance needs.
Education:
Master of Science – University of Texas at Dallas, Social Statistics – Focus on regression models, econometrics, and human capital economics.
Bachelor of Science Organizational Psychology – University of North Texas
Affiliations:
CERTIFICATIONS
Quantitative Analysis Certification – University of Texas at Dallas
Specialization in structural equation modeling, time series analysis, and Bayesian analysis.
Microsoft Certified: Azure Fundamentals 1F4724-CAFA37
PATENTS
Email Risk and Compliance: Machine Learning with Attribute Feedback Based on Express Indicators (Patent 20200364605 - 16/410412).
Candidate Contact Information:
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