Emerging Tech Standard Delivery Team Member

The Emerging Tech Standard Delivery Team drives the adoption of advanced technologies and develops enterprise-ready solutions for D&A Operations. You will partner with Operations and Technology teams to design data-driven solutions that deliver clear business and customer value.

Strategic & Technical Skill

• Hands-on technical expertise across end-to-end data science initiatives, ensuring high-quality design, development, and delivery.
• Shape and refine the product vision for advanced data management and analytics frameworks spanning data acquisition, transformation, quality, and workflow automation.
• Define optimal user experiences for financial analytics pipelines, integrating diverse tools, datasets, and services into cohesive workflows.
• Own and drive large projects from a data science perspective, removing obstacles and taking ownership to find creative solutions.
• Serve as a technical authority upholding best practice, and architectural recommendations.

Business & Stakeholder Engagement

• Partner with domain experts and senior business stakeholders to identify high-value problems and co-create AI/ML and platform strategies.
• Translate business requirements into technical specifications, solution designs, and measurable success criteria.
• Communicate complex insights, findings, and solution outcomes to product, engineering, sales, proposition, support, and leadership teams.
• Influence cross-functional teams by providing clear, data-driven recommendations and technical direction.

Advanced AI/ML Delivery & Emerging Technologies

• Design, build, and optimize production-grade AI models,including deep learning, NLP, large language models, and Retrieval-Augmented Generation (RAG).
• Demonstrate strong expertise with LLMOps and advanced MLOps frameworks, including vector databases, orchestration tools (e.g., LangChain, LlamaIndex), and scalable model-serving platforms to manage end-to-end LLM lifecycle.
• Assess third-party AI technologies, frameworks, and tools to inform build-versus-buy decisions and strengthen platform capabilities.
• Establish and uphold high coding standards, reproducibility practices, and quality controls for robust ML development.

Data Engineering & Processing Expertise

• Apply strong expertise in data extraction, including web scraping, crawling, entity recognition, and advanced pre/post-processing.
• Work with complex structured, semi-structured, and unstructured datasets,including financial documents, PDFs, and scanned content.
• Collaborate with data engineering teams to ensure scalable, reliable pipelines that support high-impact analytics workflows.

Cloud, MLOps & Deployment Excellence

• Align with modern MLOps workflows, CI/CD pipelines, and cloud-native deployment practices.
• Lead scalable deployment of ML/AI solutions on AWS, Azure, or equivalent cloud environments.
• Partner with platform engineering to enhance monitoring, observability, and full model lifecycle management.

Continuous Improvement & Innovation

• Stay abreast of emerging trends in AI, NLP, cloud computing, financial analytics, and ML engineering.
• Champion experimentation, innovation, and adoption of frontier techniques and tools.
• Identify opportunities to mature frameworks, modelling practices, and engineering processes across the organization.

Required Skills

• 8-12 years of experience in data science/AI with strong ownership of complex initiatives.
• Own end-to-end AI/ML lifecycle: problem definition, design, experimentation, deployment, and continuous improvement.
• Drive technical strategy: select models, define metrics, and balance trade-offs (performance, scalability, cost).
• Lead development of advanced AI solutions: LLMs, GenAI, multimodal systems, and RAG workflows.
• Ensure production-grade delivery: partner with Engineering for secure, scalable, and reliable deployments (MLOps/LLMOps, CI/CD).
• Define and sign off success criteria: evaluation frameworks, quality thresholds, and production readiness.
• Ensure Responsible AI & compliance: governance, explainability, documentation, and audit readiness.
• Act as technical authority: resolve risks, guide and mentor data scientists and ML engineers.
• Translate business needs into scalable AI solutions and influence cross-functional stakeholders.
• Demonstrate deep hands-on expertise in Python, ML/DL frameworks, NLP, LLMs, and RAG.
• Strong experience in MLOps/LLMOps, production deployment, and Git-based CI/CD workflows.
• Solid foundation in statistics, data engineering, and large-scale data processing.
• Proficiency in cloud platforms (Azure/AWS) and modern data ecosystems.
• Experience with AI agents, human-in-the-loop systems, and synthetic data techniques.
• Strong problem-solving, strategic thinking, and communication skills.