VP, Data Science / Machine Learning Lead - Capital Markets & Fixed Income

TWG Global AI•Santa Monica, CA

3d•$240,000 - $285,000

About The Position

At TWG Group Holdings, LLC (“TWG Global”), we drive innovation and business transformation across a range of industries, including financial services (particularly capital markets and fixed income), insurance, technology, media, and sports, by leveraging data and AI as core assets. Our AI-first, cloud-native approach delivers real-time intelligence and interactive business applications, empowering informed decision-making for both customers and employees. We prioritize responsible data and AI practices, ensuring ethical standards and regulatory compliance. Our decentralized structure enables each business unit to operate autonomously, supported by a central AI Solutions Group, while strategic partnerships with leading data and AI vendors fuel game-changing efforts in marketing, operations, and product development. Our solutions power trading desks, portfolio optimization, and risk analytics across fixed income, derivatives, and structured products. You will collaborate with management to advance our data and analytics transformation, enhance productivity, and enable agile, data-driven decisions. By leveraging relationships with top tech startups and universities, you will help create competitive advantages and drive enterprise innovation. At TWG Global, your contributions will support our goal of sustained growth and superior returns, as we deliver rare value and impact across our businesses. The Role As the Staff Machine Learning Engineer (VP) on the AI Science team, you will be responsible for architecting and deploying cutting-edge ML systems that power core business functions across the enterprise. Reporting to the Executive Director of AI Science, you will play a critical role in driving the development of scalable ML infrastructure, production-grade models, and reusable frameworks that deliver measurable business outcomes—ranging from cost optimization to top-line growth. You will bridge quantitative research and technology, with deep understanding of fixed income markets and derivatives. You will act as a technical thought leader and strategic partner in shaping the direction of the organization’s machine learning investments, fostering a culture of rigorous experimentation, reproducibility, and responsible AI.

Requirements

8+ years of experience building and deploying machine learning systems in production environments, preferably in investment banking, fixed income trading, or hedge funds, ideally within enterprise or platform-scale settings.
Proven track record of leading ML projects from ideation to production, including cross-functional collaboration and technical ownership.
Deep expertise in supervised, unsupervised, reinforcement learning or statistical modeling.
Experience with multimodal, generative AI, or large language models (e.g., LLMs, diffusion models) is a strong plus.
Proficiency in Python, along with modern ML and data stack tools (e.g., TensorFlow, PyTorch, scikit-learn, JAX, Ray, MLflow).
Hands-on experience with MLOps principles and frameworks (e.g., CI/CD pipelines for ML, model monitoring, reproducibility).
Strong understanding of cloud-based ML infrastructure (e.g., AWS SageMaker, GCP Vertex AI, or similar).
Exceptional communication and collaboration skills, with the ability to translate technical details into strategic decisions.
Strong foundation in fixed income analytics, derivatives pricing, and risk management.
Commitment to responsible AI, including model fairness, transparency, and compliance with regulatory standards.
Master’s or PhD in Computer Science, Machine Learning, Statistics, or a closely related discipline.

Nice To Haves

Hands-on experience with Palantir platforms (e.g., Foundry, AIP, Ontology) - including developing, deploying, and integrating machine learning solutions within Palantir’s data and AI ecosystem.
CFA or FRM certification

Responsibilities

Design and deploy ML systems that solve high-impact business problems for critical workflows.
Develop and implement advanced ML methods including time series forecasting, reinforcement learning, optimization algorithms, and probabilistic modeling.
Lead the adoption of emerging ML techniques and tools (e.g., generative AI, LLM fine-tuning, vector databases, RAG) through rapid prototyping.
Partner with AI researchers and data scientists to translate experimental models into production-ready systems, supporting scaling and generalizability across business domains.
Own the development of foundational models and platform capabilities that serve as building blocks for downstream AI applications across the organization.
Ensure ML models are designed with safety, fairness, and transparency in mind, and aligned with internal governance frameworks and external regulatory standards.
Collaborate with cross-functional leaders in engineering, product, and business teams to embed ML-driven decision-making into core processes and workflows.
Continuously evaluate emerging ML techniques and tools, and champion their adoption through rigorous prototyping, benchmarking, and knowledge sharing.
Define and manage metrics to evaluate model performance and business impact, ensuring ML projects meet both scientific and operational standards.
Design ML-driven pricing models for fixed income securities, derivatives, and structured products.
Mentor other ML engineers and data scientists, fostering technical excellence and a culture of innovation and collaboration.