Data Support Manager

Bank of America • Jersey City, NJ

5d • Onsite

About The Position

Under the GRM Data Strategy & Management leadership team, this hands-on role designs, builds and operationalizes quantitative models and agentic AI solutions for complex risk and data-management use cases. The practitioner works from problem framing and data discovery through experimentation, production deployment, monitoring and continuous improvement. The role partners with risk subject-matter experts, data owners, technology teams and governance partners to deliver explainable, scalable and controlled solutions in a highly regulated environment.

The successful candidate combines strong Python and data engineering skills with an understanding of data strategy, data lineage, metadata, data quality and data governance. They translate business and control objectives into production-grade data and AI products and document data provenance, model behavior, decisions and human oversight. The position operates within the GRM Data Strategy & Management team and collaborates in cross-functional Agile delivery teams.

Requirements

• Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, Engineering, Statistics, Mathematics or a related quantitative discipline.
• At least 5 years of professional experience building data analytics, machine learning or AI solutions, with demonstrated ownership from experimentation through production.
• Advanced hands-on Python development, including clean, testable and modular code.
• Experience with pandas, NumPy, scikit-learn and at least one deep-learning framework such as PyTorch or TensorFlow.
• Practical knowledge of supervised and unsupervised learning, model selection, benchmarking, error analysis and explainability.
• Hands-on experience building and deploying generative AI or agentic AI solutions using LLMs, retrieval-augmented generation, embeddings, vector search, tool calling, prompt management and multi-step orchestration using LangGraph and MCP.
• Experience designing production data or machine-learning pipelines and services using technologies such as SQL, Spark, Airflow, Databricks, REST APIs, Git, Docker and CI/CD.
• Fluency in data strategy, including authoritative-source selection, data-product thinking, target-state architecture, reuse, interoperability and business-value alignment.
• Strong working knowledge of data lineage and metadata management, including tracing data from origin through transformations and consumption, identifying controls and documenting dependencies.
• Strong working knowledge of data governance, including data ownership, classification, quality, privacy, access, retention, controls and issue remediation.
• Exposure to modern data platforms and storage patterns such as Snowflake, Databricks, Delta Lake, Apache Iceberg, Parquet, JSON or Avro.
• Experience working with relational and NoSQL stores, large and heterogeneous datasets, and structured and unstructured information.
• Ability to operate effectively in a regulated environment with strong documentation, change management, validation, testing and audit-readiness expectations.
• Exceptional written, verbal and visual communication skills, including the ability to explain complex technical topics and influence stakeholders at multiple levels.

Responsibilities

• Build agentic AI solutions using large language models, retrieval-augmented generation, tools, APIs and workflow orchestration to support research, analysis, decision support and operational processes.
• Develop robust Python services, reusable libraries, data pipelines and APIs integrating structured and unstructured data from multiple Systems of Record.
• Create evaluation frameworks for machine learning and generative AI solutions, measuring accuracy, precision and recall, robustness, latency, cost, hallucination, groundedness and task completion.
• Implement human-in-the-loop controls, guardrails, audit trails, access controls, fallback paths and approval checkpoints for agentic workflows.
• Partner with business and risk stakeholders to frame problems, define measurable success criteria and translate prototypes into scalable, production-grade capabilities.
• Apply data strategy principles to identify authoritative sources, promote reuse, minimize redundant sourcing and align solutions to target-state data architecture.
• Document and maintain end-to-end data lineage from source systems through transformations, features, models, prompts, agent actions and downstream outputs.
• Embed data governance requirements into solution design, including metadata, ownership, classifications, quality controls, retention, privacy, entitlements and issue management.
• Perform data discovery, profiling, quality assessment and root-cause analysis across large, complex datasets, and establish controls and metrics to monitor data health.
• Collaborate in cross-functional Agile delivery teams and communicate technical concepts, trade-offs, risks and outcomes to technical and executive audiences.
• Mentor team members, contribute reusable patterns and standards, and help advance engineering discipline across GRM data and AI initiatives.

Benefits

• Access to paid time off.
• Industry-leading benefits and resources and support for employees.
• Eligible to participate in an annual discretionary incentive plan, with awards based on individual performance and behaviors, line-of-business or group performance and contributions, and overall company success.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior