Deputy Chief Data Scientist (Remote)

At Broadridge, we've built a culture where the highest goal is to empower others to accomplish more. If you're passionate about developing your career, while helping others along the way, come join the Broadridge team.

Broadridge is hiring a Deputy Chief Data Scientist to lead the design, development, and modernization of a next-generation integrated platform supporting Customer Communications, Securities Processing and Wealth Management. This platform underpins critical business capabilities, including large-scale data ingestion, composition, extraction, and loading across global markets.

You will lead a globally distributed engineering organization and be responsible for transforming legacy platforms into a modern, scalable, secure, cloud-native ecosystem. The role combines deep technical leadership, architectural vision, and proven people leadership in high-volume, mission-critical environments.

Key Responsibilities

Technology & Architecture Leadership

• Lead the architecture of large-scale AI and machine learning platforms, enabling advanced model development, training, inference, and continuous improvement across both streaming and batch data environments.
• Oversee the end-to-end data and ML architecture, including feature engineering pipelines, model-ready data processing, ETL/ELT workflows, and seamless integration of diverse internal and external data sources to power AI workloads.
• Define cloud-native AI/ML architectural patterns using AWS and GCP-leveraging containerized and serverless compute, distributed training infrastructure, GPU/accelerated environments, and infrastructure-as-code to enable scalable and secure AI operations.
• Champion event-driven and distributed AI architectures, incorporating real-time data feeds, model-triggered workflows, observability tailored for ML systems, and strong governance frameworks to ensure reliability, fairness, and responsible AI practices.
• Collaborate across engineering, data science, MLOps, and product teams to drive architectural cohesion, align AI capabilities with business strategy, and ensure platforms support experimentation, rapid prototyping, and production-grade AI deployment.

• Architect and deploy end-to-end ML and AI systems in cloud environments (AWS/GCP), from data ingestion through model deployment, monitoring, and lifecycle management.
• Build and operationalize deep learning, reinforcement learning, and AI solutions across domains such as advertising, finance, and cybersecurity.
• Develop models and frameworks for ranking, optimization, risk assessment, and behavioral anomaly detection.
• Deliver enterprise-grade NLP and LLM capabilities, including retrieval-augmented pipelines, document intelligence, entity extraction, and multilingual processing.
• Integrate generative AI into workflows to enhance classification, semantic retrieval, and knowledge-driven automation.
• Design and operate big-data ecosystems using technologies such as Hadoop, Spark, and modern search/analytics frameworks.
• Engineer scalable data platforms leveraging both relational and NoSQL databases, supporting performance-focused operational and analytical workloads.
• Build cloud-native AI services that support distributed training, scalable inference, and hybrid batch/streaming workloads.
• Establish mature MLOps practices including model CI/CD, feature pipelines, monitoring, drift detection, and automated retraining.
• Lead experimentation and model optimization through structured A/B testing and advanced evaluation frameworks.
• Deliver AI-driven cybersecurity, fraud detection, and risk-analytics systems incorporating anomaly detection, fuzzy matching, clustering, and related techniques.
• Build production-grade software using languages such as Python, Java, R, C/C++, Scala, SQL, and Shell, with experience developing high-performance backend and distributed systems.
• Lead high-performing global engineering and applied-science teams, fostering a culture of excellence, innovation, and customer-focused delivery.
• Partner with product, business, and operations leaders to align AI and engineering initiatives with strategic outcomes and organizational goals.

• Minimum 15 years of experience in AI, ML, and software engineering, including leadership of global, cross-functional technical teams.
• Proven track record designing and building large-scale AI/ML platforms, distributed systems, and cloud-based architectures.
• Strong background in data architecture, ETL/ELT pipelines, and big-data ecosystems.
• Experience delivering solutions in mission-critical environments across industries such as advertising, finance, and cybersecurity.
• Deep understanding of distributed systems, API-centric design, and event-driven architecture.
• Demonstrated ability to lead complex, multi-team transformation initiatives.
• Experience mentoring and developing high-performing engineering and applied-science organizations.
• Advocacy for AI-assisted development practices to enhance engineering velocity, quality, and innovation.

• Experience delivering technology solutions in regulated or compliance-oriented environments, including financial services or cybersecurity.
• Strong understanding of DevOps, SRE, and modern observability practices.
• Experience collaborating with globally distributed teams across multiple time zones.

• Strategic thinker with strong execution discipline and an ability to balance long-term vision with near-term delivery.
• Clear, influential communicator capable of simplifying complex technical topics for executive and business audiences.
• Inclusive, results-driven leader who builds trust, fosters accountability, and drives organizational excellence.