Senior Associate - Machine Learning Engineer

As a Senior Associate, Data Scientist / Machine Learning Engineer within our ASSET & WEALTH MANAGEMENT (AWM) AI/ML team, you will contribute to the design, development, and production of next-generation AI/ML systems across pricing, campaign targeting, personalization, and related business use cases. This is a hands-on technical individual-contributor role for an exceptional engineer who blends broad, deep AI/ML expertise with a strong emphasis on agentic AI development together with strong machine learning engineering and software engineering skills. Beyond building models and agents, you will help build robust, scalable systems, contribute to technical direction under the guidance of senior team members, collaborate closely with engineers and stakeholders, and help translate complex business problems into production-grade solutions that drive measurable impact. Job Responsibilities Contribute to the end-to-end design, development, and deployment of AI/ML and agentic AI systems from problem framing and experimentation through production, monitoring, and continuous improvement. Design and build agentic AI workflows and applications, including multi-step reasoning, tool use, orchestration, and LLM-based agents that automate and augment complex analytical and business processes. Help build and maintain scalable, reliable ML and agentic infrastructure and pipelines, applying strong software engineering practices (version control, testing, CI/CD, code review, modular design). Apply broad AI/ML methods including deep learning, generative AI/LLMs, and modern ML techniques to high-impact business problems in pricing, marketing, campaign targeting, and customer analytics. Contribute to technical standards and best practices, championing engineering excellence, reproducibility, and responsible AI/ML practices. Collaborate with data scientists and engineers, participating in code reviews and knowledge sharing, and supporting the mentoring of junior or intern staff as appropriate. Partner with product, business, and engineering teams to scope opportunities and communicate technical trade-offs and results to both technical and non-technical audiences. Support and apply model validation, governance, monitoring, and risk-management frameworks for models and agents in production. Required qualifications, skills and capabilities Solid experience (typically 4+ years ) building, deploying, and scaling machine learning and AI systems in production environments. Strong, hands-on experience developing agentic AI systems including LLM-based agents, tool/function calling, multi-step reasoning, orchestration frameworks, and AI-assisted analytical workflows. Broad and deep expertise across modern AI/ML methods, including machine learning, deep learning, generative AI/LLMs, and statistical modeling. Deep, hands-on software engineering expertise: strong proficiency in Python (and/or other production languages), software design principles, testing, version control, CI/CD, and building production-grade code. Strong machine learning engineering skills, including experience with ML frameworks, model pipelines, feature stores, orchestration, and MLOps tooling. Strong quantitative training in Statistics, Data Science, Computer Science, Economics, Applied Mathematics, Operations Research, or a related field. Proven experience with model validation, diagnostic testing, and careful interpretation of model performance. Demonstrated ability to own and deliver technical projects and collaborate effectively within a team. Excellent ability to communicate complex technical findings clearly to both technical and non-technical audiences. Strong problem-solving skills and the ability to work independently in ambiguous, real-world settings. Preferred qualifications Experience building and deploying advanced agentic AI platforms, multi-agent systems, or retrieval-augmented generation (RAG) applications at scale. Industry experience applying AI/ML to pricing, marketing, campaign targeting, personalization, or customer analytics. Understanding of causal inference fundamentals (e.g., confounding, selection bias, treatment effect estimation) and modern causal ML methods such as meta-learners, uplift models, causal forests, or double machine learning is a good-to-have. PhD or a Master's in Statistics, Computer Science, Economics, Econometrics, or a related quantitative field is a plus.