Senior Specialist – AI-ML Engineer

Applies and integrates statistical, mathematical, predictive modelling and business analysis skills to manage and manipulate high volume data from a variety of sources and able to identify business opportunities from the insights and predictions that result from that analysis. Collates, models, interprets and analyses data, explains variances and trends and recommends business opportunities from that analysis. Works on and off the cloud.

• Applies and integrates statistical, mathematical, predictive modeling and business analysis skills to manage and manipulate complex high volume data from a variety of sources and to lead projects in identifying business growth opportunities for Nasdaq and their clients.  Is working across business units.
• Stays abreast of new technologies and brings best practice thinking into review of internal and external analytical techniques used, processes, and tools.
• Coaches and leads work with more junior team members on building and prototyping data visualizations; creating computer models, and building data visualizations. 
• Is a functional expert in working in the cloud to conduct scalable data research;  completes modelling and business analysis and develops experiments to test potential deployments
• Works closely with product sponsors to understand business needs and propose solutions:
  • Use domain knowledge and analytical expertise to suggest new product ideas
  • Rapidly conduct experiments to validate new product ideas
  • Write research reports describing the experiment conducted, results, and findings.
  • Based on these results, recommends actions to technology, product, and senior management.
• Partners with BU’s to brainstorm ideas on new opportunities; provides constructive feedback to the technology  and business teams on their business growth ideas, based on analytics and modelling.
• As a functional leader takes ownership of new projects and initiatives and mentors junior engineers in the team.
• Writes and publishes white papers for industry review.

Additionally:

• Design, develop and deploy generative AI solutions for enterprise applications, AI Agents, AI workflows
• Architect and implement large language models (LLMs) and other generative models
• Optimize model performance, efficiency, and computational resources
• Develop API integrations and microservices for AI model deployment
• Ensure responsible AI practices including bias detection and mitigation
• Implement security measures and data privacy controls
• Create monitoring systems for model performance and drift detection