Lead Quantitative Analytics Specialist - AI and Machine Learning

About the Role

Key Responsibilities

• Lead the design, development, and implementation of NLP and LLM-based systems for complex language understanding tasks.
• Architect scalable and robust solutions using state-of-the-art NLP techniques and open-source or vendor frameworks.
• Guide and mentor junior team members, fostering technical excellence and collaboration.
• Partner with cross-functional teams to gather requirements and deliver impactful AI-driven solutions.
• Engage with regulators, auditors, and technical stakeholders to ensure transparency and compliance.
• Stay abreast of emerging trends in NLP, LLMs, and GenAI, and translate research into practical applications.
• Drive innovation through proof-of-concept initiatives and exploratory research.
• Establish and promote best practices for model development, evaluation, and deployment.
• Build and operationalize AI/ML models using modern NLP libraries and cloud-native tools.

Requirements

• 5+ years of Quantitative Analytics experience, or equivalent demonstrated through work experience, training, military experience, or education.
• Master's degree or higher in a quantitative discipline such as mathematics, statistics, engineering, physics, economics, or computer science.

Nice to Have

• Hands-on experience with GenAI frameworks and tools.
• Deep understanding of ML, LLMs, and GenAI technologies.
• Strong programming skills in Python and experience with NLP libraries (NLTK, spaCy, Hugging Face).
• Experience in developing NLP models like logistic regression, XGBoost, LightGBM's linear SVC, etc.
• Demonstrated experience fine-tuning and deploying LLMs (GPT, BERT, T5, etc.).
• Experience with prompt engineering and optimization techniques.
• Knowledge of model evaluation metrics and performance optimization.
• Experience leading technical teams and managing complex projects.
• Experience with multimodal models and retrieval-augmented generation.
• Contributions to open-source NLP/LLM projects or research publications.
• Experience with MLOps and model deployment pipelines.
• Knowledge of responsible AI practices and bias mitigation techniques.
• Experience with cloud platforms (GCP, Azure) for ML workloads.
• Background in specific domain applications (healthcare, finance, legal, etc.).
• Experience with distributed computing for large-scale model training.

Qualifications

• Master's degree or higher in a quantitative discipline such as mathematics, statistics, engineering, physics, economics, or computer science.

Benefits & Perks

• Health benefits
• 401(k) Plan
• Paid time off
• Disability benefits
• Life insurance, critical illness insurance, and accident insurance
• Parental leave
• Critical caregiving leave
• Discounts and savings
• Commuter benefits
• Tuition reimbursement
• Scholarships for dependent children
• Adoption reimbursement