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Wealth Management Operations- Process Modernization-Associate-Dallas
About the position
At Goldman Sachs, we connect people, capital and ideas to help solve problems for our clients. We are a leading global financial services firm providing investment banking, securities and investment management services to a substantial and diversified client base that includes corporations, financial institutions, governments and individuals. For us, it’s all about bringing together people who are curious, collaborative and have the drive to make things possible for our clients and communities. Operations is a dynamic, multi-faceted division that partners with all areas of the firm to deliver banking, sales and trading and asset management capabilities to clients around the world. Alongside this vital service delivery role, Operations provides essential risk management and control, preserving and enhancing the firm’s assets and its reputation. Operations partners with all areas of the firm to deliver banking, sales and trading and asset management capabilities to clients around the world. Alongside this vital service delivery role, Operations provides essential risk management and control, preserving and enhancing the firm’s assets and its reputation. Operations span all product lines and markets, and functions. As a Process Modernization Associate, you will participate in a sophisticated, high-stakes initiative to build a digital twin of the organization’s operational landscape. You will bridge the gap between deep behavioral data, resource allocation, and strategic intent to create a self-optimizing organizational health model. This role is a \"Hybrid Architect\" position, shifting the organization from manual bottleneck identification to an AI-driven transformation engine.
Responsibilities
- Strategic Transformation via AI Engineering
- AI-Enhanced Transformation : Participate in global transformation initiatives by deploying multi-step prompt architectures to perform deep-dive root cause analysis and strategic synthesis.
- Context Engine Architecture : Design and implement AI Context Engines for specific roles (e.g., Process Re-engineer, Data Analyst) to boost output accuracy and compress discovery-to-design cycles.
- Taxonomy-Led Pattern Solving : Develop and utilize a standardized organizational taxonomy to identify and solve for cross-functional patterns and inefficiencies across the value chain.
- Business Resiliency & Risk Planning : Integrate regulatory control points and audit logic directly into the transformation framework to proactively minimize systemic risks and ensure a robust automated controls environment.
- Process Re-Engineering & Intelligent Discovery
- Empirical Process Discovery : Utilize process mining and task mining tools to identify the delta between \"perceived\" workflows and actual data-led reality.
- Unified Data Fabric : Oversee the integration of disparate data silos—including FTE allocations and product roadmaps—into a structured format for machine-led ingestion.
- Automation & Collaborative Operating Model : Architect an operating model that seamlessly integrates AI agents and human expertise, identifying high-impact agentic opportunities to enhance decision-making and straight-through processing.
- Human-in-the-Loop Design : Design workflows where AI handles high-volume analysis while human experts focus on validating AI-prioritized opportunities and complex decision-making.
Requirements
- Bachelor’s degree in Science, Technology, a relevant Engineering degree, including Engineering Management, Electronics Engineering, Mathematics, or a related field.
- Two years of experience in this area or in a related role
- Work experience with: End-to-end system design using a combination of low-code and high-code tools and programming language
- Competency in languages such as Python and SQL
- Data analysis and integration tools (e.g. Alteryx, Knime), Visualization tools (e.g. Tableau, Qliksense) and workflow tools
- Building robust and efficient ETL pipelines.
- Designing and implementing data governance, and SDLC models.
- Building web crawlers for data analytics and data centralization.
- Integrating with APIs.
- Utilizing Git, version control, unit testing, and industry standard documentation methods
- Holding and moderating technical conversations with technical and non-technical audiences, ability to explain complex problems in layman terms
- Assessing and improving operations or business processes to improve efficiency, client service and risk profiles
