Rocket Software

Senior Technical Director- ML & Algorithms

Rol remoto de Machine Learning Engineering con fit claro de ubicación del candidato.

Publicado7 jul 2026

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Actualidad de la fuente7 jul 2026

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Match de stackJava, Python

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Resumen del rol

Senior Technical Director- ML & Algorithms

Requisitos y responsabilidades

Contenido del rol extraído en secciones para revisar más rápido.

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 insights are explainable, inspectable, and aligned with governance and compliance expectations, particularly in regulated and mission-critical environments.
Collaborate closely with domain experts in mainframe operations, performance, security, and data platforms to translate deep system knowledge into scalable, data-driven intelligence.
Lead across reporting boundaries by influence and technical credibility rather than hierarchy, helping teams solve complex problems at all stages of feature development and delivery.
Partner with Product and Architecture leadership to shape long-term strategy for operational analytics, AI-assisted modernization, and analytics platform evolution.
Engage with enterprise customers to understand real-world operational and analytical constraints and validate ML-driven solutions.
Mentor Principal Engineers and senior technical staff, raising the bar for ML rigor, systems thinking, and operational relevance across Rocket.
Contribute internal and external thought leadership on applied ML for mainframe operations, enterprise analytics, and responsible AI.

Required Qualifications

12+ years of experience in software engineering, ML systems, or advanced algorithm development.
Proven experience owning and delivering ML or algorithmic systems into production at scale.
Strong background in large datasets, distributed processing, and end-to-end ML system design.
Solid coding skills in Python and Java, with foundations in optimization, statistics, and applied mathematics.
Experience building ML or analytics solutions tied to system behavior, performance, observability, or operational telemetry.
Demonstrated ability to operate effectively across teams and organizations through technical leadership and influence.
Track record of mentoring senior technical talent and working as a player-coach.
Preferred Qualifications
Strong experience with mainframe systems and data sources such as z/OS, SMF, RMF, Db2, RACF, or mainframe performance analytics.
Experience with analytics databases or in-database ML systems, including model training, evaluation, and execution close to data.
Familiarity with regression, classification, clustering, and time-series techniques used in production analytics systems.
Experience designing explainable, auditable, or compliance-aware ML solutions.
Familiarity with distributed computing frameworks or ML platforms such as Spark, Flink, Ray, or cloud ML pipelines.
PhD in Computer Science, Machine Learning, or a related field.
Publications, patents, or conference contributions in ML, algorithms, systems analytics, or operational monitoring.