Machine Learning Scientist

About the Role

Key Responsibilities

• Research and explore new machine learning methods through independent study, attending industry-leading conferences and experimentation.
• Develop state-of-the-art machine learning models to solve real-world problems and apply them to complex business-critical issues in Cybersecurity, Software, and Technology Infrastructure.
• Collaborate with multiple partner teams to deploy solutions into production.
• Drive firmwide initiatives by developing large-scale frameworks to accelerate the application of machine learning models across different areas of the business.
• Contribute to reusable code and components that are shared internally and externally.

Requirements

• PhD in a quantitative discipline (e.g., Computer Science, Electrical Engineering, Mathematics, Operations Research, Optimization, or Data Science) with 1 year of experience, or a Master's degree with 2 years of industry or research experience.
• Hands-on experience and solid understanding of machine learning and deep learning methods.
• Extensive experience with machine learning and deep learning toolkits (e.g., TensorFlow, PyTorch, NumPy, Scikit-Learn, Pandas).
• Experience with large language models (LLMs) and related tools & techniques (e.g., LangChain, LangGraph, Vector databases, open-source Models, RAG, Agentic Systems & Workflows, LLM fine-tuning).
• Ability to design experiments and training frameworks, and evaluate model performance metrics aligned with business goals.
• Experience with big data and scalable model training.
• Strong communication skills to effectively convey technical concepts to both technical and business audiences.
• Curious, hardworking, detail-oriented, and motivated by complex analytical problems.
• Ability to work independently and collaboratively.

Nice to Have

• Strong background in Mathematics and Statistics.
• Familiarity with the financial services industry.
• Experience with A/B experimentation and data/metric-driven product development.
• Experience with cloud-native deployment in large-scale distributed environments.
• Published research in Machine Learning, Deep Learning, or Reinforcement Learning.
• Ability to develop and debug production-quality code.
• Familiarity with continuous integration models and unit testing.

Qualifications

• PhD in a quantitative discipline with 1 year experience or Masters with 2 years of industry or research experience.

Benefits & Perks

• Competitive total rewards package including base salary, commission-based pay, and discretionary incentive compensation.
• Comprehensive health care coverage.
• On-site health and wellness centers.
• Retirement savings plan.
• Backup childcare.
• Tuition reimbursement.
• Mental health support.
• Financial coaching.