Quantitative Analytics Manager

About the Role

Management and Governance

• Manage quantitative analysts and credit risk analysts.
• Mitigate operational risk and compute capital requirements.
• Set work scope and priorities with experienced management.
• Develop strategies, policies, procedures, and organizational controls with model, technology, and validation stakeholders.
• Resolve operational-risk issues and support business decision-making.
• Interact with auditors and regulators.
• Manage quantitative-analytics people and financial resources.
• Mentor direct reports and assist with hiring.

Required Qualifications

• At least 5 years of quantitative analytical experience or equivalent demonstrated through work experience, training, military experience, or education.
• At least 2 years of leadership experience.
• A master's degree or higher in mathematics, statistics, engineering, physics, computer science, or another quantitative discipline.

Desired Qualifications

• A master's or PhD in computer science, machine learning, artificial intelligence, engineering, or a related quantitative field.
• Experience in AI or machine-learning model development.
• Experience building foundation models, generative AI, and agentic AI applications.
• Strong quantitative, analytical, communication, business, data-management, and risk-control capabilities.

Technical Knowledge and Experience

• Foundation-model training experience may include pre-training, supervised fine-tuning, RLHF, RLAIF, PPO, DPO, and GRPO.
• Experience training and deploying models in cloud environments, including GCP.
• Experience with multi-agent architectures and frameworks such as LangChain, LangGraph, LlamaIndex, Google's ADK, and CrewAI.
• Experience with RAG applications, distributed GPU training, LoRA, and PEFT.

Job Expectations

• Design, pre-train, fine-tune, and evaluate transformer-based, foundation, and small language models.
• Develop and deploy AI and machine-learning solutions across generative AI, agentic, and traditional applications.
• Build LLM-powered agents and multi-agent systems with planning, reasoning, orchestration, and human-in-the-loop collaboration.
• Monitor production models and AI systems for performance, stability, and model drift.
• Build scalable model training and deployment pipelines.
• Improve model performance through distributed computing, distillation, quantization, and pruning.
• Optimize inference speed, latency, throughput, and cost.

Posting Information