Vice President, Data Science

Aegistech; New York (NY); On-site; USD 180,000 - 240,000; Full time

Job summary

Enterprise Solutions (ES) Technology is seeking a senior leader to define and drive the AI/ML and data science roadmap for a regulated global financial-services environment.

You will design, build, and operate production-grade models for anomaly detection, forecasting, and behavioral signals, ensuring accuracy, explainability and regulator readiness. The role requires hands-on expertise and strategic guidance across teams.

Qualifications

• 25+ years of experience in analytics, data science and ML in regulated environments.
• Strong lifecycle expertise: modelling, deployment, monitoring, and governance.
• Hands-on with data, code, ML frameworks, and systems engineering.
• Ability to translate complex problems into scalable ML solutions.

Responsibilities

• Define and lead the AI/ML roadmap for Enterprise Solutions in a regulated global finance context.
• Design, build, and operate production-grade models for anomaly detection, forecast, and behavioral signals.
• Own full model lifecycle: framing, exploration, back-testing, deployment, monitoring, and incident response.

Skills

Leadership in ML/AI

Production ML systems

Regulatory/compliance awareness

Communication with stakeholders

Hands-on data science

Education

Advanced degree in a quantitative field

Tools

Python

SQL

TensorFlow/PyTorch

Cloud platforms

Job description

Enterprise Solutions (ES) Technology is a powerful combination of world-class people and businesses with a shared mission and legacy of evolving markets and delivering for the most demanding customers in the world.

We are the catalyst to bring data, software, services, and expertise together to power the markets of the future. We drive growth through world-class execution, a growth mindset, and our unique expertise and passion for building innovative and connected solutions. We collaborate with our business line to drive business growth by focusing on execution of our joint product strategies. We ensure that we maximize business value while at the same time minimizing the cost of ownership of our technology estate.

Responsibilities and Impact:

• Define and lead the AI/ML and applied data science roadmap for Enterprise Solutions, shaping high-rigor analytical capabilities across a complex, regulated global financial-services environment.
• Design, build, and operate production-grade models for anomaly detection, variance analysis, drift detection, forecasting, behavioral signals, and prediction, ensuring they are accurate, explainable, resilient, and regulator-ready.
• Own the full model lifecycle from problem framing, data exploration, feature engineering, back‑testing, validation, deployment, monitoring, tuning, and incident response when models degrade or fail in production.
• Partner closely with engineering, data platform, product, and business teams to translate complex analytical problems into scalable, production‑ready ML systems that deliver measurable operational and business outcomes.
• Act as a hands‑on technical authority and multiplier, reviewing models, guiding senior practitioners, improving data quality and monitoring standards, and influencing decisions through deep expertise rather than formal authority.
• Gain high‑impact exposure to senior technology leaders, market‑facing financial‑services platforms, regulated AI/ML practices, and global enterprise‑scale transformation, with the opportunity to shape how advanced analytics and machine learning are applied safely and effectively in production.

What we are looking for:

• 25+ years of experience in analytics, data science, and machine learning, with a strong track record of delivering and operating production‑grade models within financial services or other highly regulated, high‑availability environments.
• Deep expertise in applied data science and ML, including statistical modelling, predictive analytics, model development, validation, deployment, monitoring, and optimisation across the full model lifecycle.
• Strong hands‑on technical skills, with the ability to work directly with data, code, ML frameworks, and complex datasets, while partnering effectively with software engineering and platform teams.
• Experience building scalable, reliable, and explainable AI/ML solutions that meet operational, governance, and regulatory requirements and deliver measurable business outcomes.

Additional Preferred Qualifications:

• Pragmatic and outcome‑oriented mindset, focused on delivering models that perform reliably in production and create tangible business value.
• Excellent communication and stakeholder management skills, with the ability to clearly explain modeling decisions, assumptions, risks, and limitations to both technical and non‑technical audiences.
• Influential leadership and collaboration, capable of driving alignment and technical excellence across cross‑functional teams without relying on formal authority.
• Coaching and mentoring capability, guiding and developing data scientists through technical reviews, knowledge sharing, hands‑on support, and leading by example.