Staff / VP, Data Science - Marketing & Sales Focus

TWG Global AI•New York, NY

12d•Hybrid

About The Position

The Organization 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 Data Scientist (VP) on the AI Science team, you will be responsible for designing and deploying production AI systems that power operations, sales, and marketing workflows for consumer-facing businesses across a diversified portfolio. Reporting to the Executive Director of AI, you will play a critical role in building AI-powered products that deliver measurable business outcomes for senior business stakeholders. You will operate with unusual scope and speed: greenfield problems across multiple businesses, direct access to executive decision-makers, and work that ships to production in weeks, not quarters. You will operate as a forward-deployed data scientist, embedded directly with the businesses you serve, from problem discovery through production deployment—combining deep expertise in predictive modeling, experimentation, and optimization with hands-on ability to build and ship LLM-powered products at speed. You will have direct visibility to senior business stakeholders, translating complex business workflows into working AI systems. This is a hands-on technical leadership role: you will spend the majority of your time designing, building, and shipping systems, while owning end-to-end delivery and the client relationship for your engagements—helping shape the direction of the organization's AI investments and fostering a culture of rapid iteration, rigorous evaluation, and responsible AI.

Requirements

7+ years of experience building and deploying ML or data science systems in production environments.
Proven track record of leading AI/ML projects from ideation to production, including cross-functional collaboration, technical ownership, and direct engagement with business stakeholders.
Hands-on experience building production LLM applications: prompt engineering, orchestration (e.g., multi-agent systems, chained workflows), retrieval-augmented generation, structured output parsing, and evaluation pipelines.
Deep expertise in predictive modeling, experiment design and causal inference (A/B testing, uplift measurement), and optimization, with strong statistical foundations.
Experience working with business-generated data — customer, campaign, transaction, or operational — and turning it into decisions alongside the commercial owners of that data.
Proficiency in Python, along with modern AI/ML tools (e.g., PyTorch, scikit-learn, LangChain/LangGraph, vector databases, LLM APIs), with working knowledge of MLOps practices (CI/CD, model monitoring, evaluation) and cloud infrastructure (AWS, GCP, or similar).
Exceptional communication and collaboration skills, with the ability to translate technical details into strategic decisions and present directly to senior business stakeholders.
Master's (or MBA) or PhD in Computer Science, Machine Learning, Statistics, Operations Research, or a closely related discipline preferred.

Nice To Haves

Hands-on experience with enterprise data and AI platforms (e.g., Palantir Foundry or similar)—including developing, deploying, and integrating AI solutions within an integrated data ecosystem.
Experience applying data science to marketing, sales, or operations problems—e.g., marketing attribution and media-mix modeling, funnel and cohort analytics, customer segmentation and lifetime value, demand forecasting, or pricing optimization.
Familiarity with the systems marketing data lives in — CRM, CDP, ad platforms, web/product analytics — and the messiness that comes with them.
Prior experience in a forward-deployed, consulting, or client-facing data science capacity.

Responsibilities

Own high-impact business problems from ambiguity to production: work directly with operating teams to frame the question, build the models, systems, and applications that answer it, and ship it into daily workflows across operations, sales, and marketing.
Build predictive models, experimentation frameworks, and optimization systems for marketing, sales, and operations—turning campaign, funnel, and operational data into decision-ready insight for business leaders.
Build production LLM applications end-to-end: structured extraction from complex documents and customer interaction data, retrieval-augmented generation, multi-step orchestration, and rigorous evaluation.
Evaluate and champion emerging AI techniques and tools (e.g., agentic workflows, LLM evaluation frameworks, vector databases, RAG architectures) through hands-on prototyping, benchmarking, and iterative deployment.
Partner with AI researchers, data scientists, and domain experts to translate experimental models into production-ready systems—hardening prototypes for latency, cost, accuracy, and reliability while generalizing solutions across multiple business domains.
Contribute reusable AI capabilities and methods that serve as building blocks across the organization, partnering with engineering to set the standards for how AI systems are built, evaluated, and maintained.
Own client relationships end-to-end: collaborate directly with senior business stakeholders to understand workflows, scope and deliver engagements, gather feedback, and iterate on AI products that meet practitioner-grade quality standards.
Mentor engineers and data scientists through design reviews, code reviews, and hands-on pairing—raising the bar for technical excellence and engineering rigor across the team.