Senior Technical Director - ML & Algorithms

It's fun to work in a company where people truly BELIEVE in what they're doing!

Job Description Summary:

Rocket Software , a leading provider of enterprise modernization software, is expanding its AI strategy in support of its vision of Modernization Without Disruption. With established offerings such as SmartChat, Anomalytics, and Rocket EVA, Rocket has deep experience applying AI to complex, mission-critical enterprise systems.

Rocket is seeking a Senior Technical Leader to join its AI Center of Excellence (AI COE) and shape the machine learning and algorithmic foundations of its mainframe operational analytics products. The Senior Technical Director for Machine Learning & Algorithms will define and drive how large-scale ML systems and advanced algorithms are applied to mainframe operational data to deliver monitoring, analysis, optimization, risk detection, and modernization insights.

This role is deeply grounded in operations and telemetry with a focus on extracting signal from high-volume, high-fidelity sources such as SMF, RMF, log streams, workload metrics, and system events. The role is hands-on and operates as a player-coach, working directly with enterprise teams across the full lifecycle of feature development. The role leads through technical expertise and influence across reporting boundaries and helps establish consistent best practices across Rocket. Come join our team for an opportunity to shape the application of machine learning across Rocket and the broader enterprise modernization landscape.

Essential Duties & Responsibilities

• Define and lead Rocket’s machine learning and algorithmic strategy across Infra, App and Data modernization business lines.
• Extend that strategy across Rocket’s enterprise analytics platforms, including in-database and embedded ML capabilities, to ensure architectural consistency and reuse.
• Act as a hands-on technical leader working with product and engineering teams throughout the full feature lifecycle, from problem framing and model selection to validation, delivery, and operationalization.
• Architect large-scale ML and data processing systems that operate on high-volume operational data, including z/OS telemetry, SMF/RMF records, logs, workload metrics, and enterprise analytics datasets.
• Design and implement advanced algorithms and statistical models for anomaly detection, pattern analysis, forecasting, optimization, classification, and modernization insights.
• Guide the use and evolution of in-database ML and analytics capabilities, including regression, classification, clustering, and time-series techniques, ensuring they are applied correctly, efficiently, and at scale.
• Establish ML architectures, technical standards, and best practices that span feature engineering, model training, evaluation, explainability, deployment, and lifecycle management.
• Ensure ML-driven ins