Vice President, Data Science

Aegistech; New York (NY); On-site; USD 200,000 - 350,000; Full time

Job summary

Enterprise Solutions Technology is seeking a seasoned analytics leader to drive the AI/ML and applied data science roadmap in a regulated financial-services environment. You will design, deploy, and monitor production-grade models for anomaly detection, forecasting, and behavioural signals while ensuring regulatory readiness.

You will own the full lifecycle, collaborate with engineering and business teams, and act as a hands-on technical authority to guide senior practitioners and improve data

Qualifications

• 25+ years of experience in analytics, data science, and ML within regulated environments.
• Proven track record delivering production-grade ML models.
• Experience building scalable, explainable AI/ML solutions.
• Ability to partner with engineering, product, and business teams.

Responsibilities

• Define and lead the AI/ML roadmap for a regulated financial services context.
• Design, build, and operate production-grade models for anomaly detection and forecasting.
• Own full model lifecycle from framing to monitoring and incident response.
• Collaborate with cross-functional teams to translate analytics problems into scalable ML systems.
• Provide technical leadership, review models, and influence decisions through expertise.

Skills

Analytics

Data science

Machine Learning

Statistical modelling

Model deployment

Explainable AI

Regulatory compliance

Stakeholder management

Production ML systems

Tools

ML frameworks

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.