The Emerging Technology Standard Delivery team is responsible for driving the adoption of cutting-edge AI technologies and delivering enterprise-grade Data & Analytics solutions. As a Principal Data Scientist, you will collaborate with Technology, Engineering, and Operations teams to develop innovative, data-driven products that create measurable business value.

This role requires deep expertise in Data Science, Machine Learning, Natural Language Processing (NLP), Deep Learning, and Generative AI, along with the ability to understand financial datasets and business workflows. The ideal candidate will have hands-on experience with Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI Agents, Multimodal AI, LLMOps, MLOps, and Synthetic Data, enabling the design and deployment of scalable AI solutions for enterprise environments.

Key Responsibilities :

AI & Data Science Leadership :

- Lead end-to-end design, development, and deployment of advanced AI and Data Science solutions.

- Drive technical strategy and architecture for enterprise-scale Machine Learning and AI initiatives.

- Define and enhance data analytics platforms covering data acquisition, transformation, quality management, and workflow automation.

- Lead complex AI programs by identifying technical challenges and delivering innovative, scalable solutions.

- Establish best practices, coding standards, architecture guidelines, and model governance across the organization.

Business Partnership & Stakeholder Management :

- Collaborate with business leaders, product teams, and domain experts to identify high-impact AI opportunities.

- Translate business objectives into scalable AI/ML solutions and measurable outcomes.

- Present technical concepts, insights, and recommendations to executive leadership and cross-functional stakeholders.

- Influence strategic technology decisions through data-driven recommendations and AI expertise.

Advanced AI & Machine Learning :

- Design, develop, optimize, and deploy production-ready AI models using Deep Learning, NLP, Generative AI, LLMs, and RAG architectures.

- Implement LLMOps and advanced MLOps practices, including vector databases, orchestration frameworks such as LangChain and LlamaIndex, and scalable model serving platforms.

- Build enterprise solutions leveraging Multimodal AI models capable of processing text, images, audio, and video.

- Evaluate emerging AI tools, frameworks, and third-party platforms to support build-versus-buy decisions.

- Drive continuous model evaluation, optimization, monitoring, and performance improvements.

- Apply AI Agents and Human-AI collaboration frameworks to improve productivity and business outcomes.

- Utilize Synthetic Data techniques to improve model robustness, privacy preservation, and training efficiency.

Data Engineering & Analytics :

- Develop solutions involving web scraping, data extraction, crawling, entity recognition, and advanced data preprocessing.

- Process structured, semi-structured, and unstructured data, including financial reports, PDFs, scanned documents, and textual content.

- Partner with Data Engineering teams to build scalable and reliable data pipelines supporting enterprise analytics.

Cloud, MLOps & Deployment :

- Build and deploy AI solutions using cloud platforms such as AWS and Azure.

- Implement CI/CD pipelines and MLOps best practices for automated model deployment and lifecycle management.

- Collaborate with Platform Engineering teams to enhance monitoring, observability, and model governance.

Innovation & Research :

- Stay current with emerging advancements in AI, Machine Learning, NLP, Cloud Computing, and Financial Analytics.

- Promote innovation through experimentation and adoption of next-generation AI technologies.

- Continuously improve AI frameworks, engineering standards, and data science methodologies.

Team Leadership & Capability Development :

- Mentor and coach Data Scientists while promoting technical excellence and innovation.

- Collaborate with Engineering and Operations teams to deliver enterprise-scale AI solutions.

- Define long-term capability roadmaps covering AI tools, frameworks, skills, and best practices.

- Foster a culture of scientific rigor, engineering excellence, and continuous learning.

Required Skills & Experience :

- 12+ years of experience in Data Science, Machine Learning, Artificial Intelligence, or Advanced Analytics, including leadership of large-scale AI initiatives.

- Proven expertise in designing and scaling enterprise AI platforms and Machine Learning solutions.

- Strong knowledge of NLP, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Generative AI, Multimodal AI, and modern AI architectures.

- Experience defining AI strategies, technical roadmaps, and enterprise transformation initiatives.

- Hands-on expertise with AI Agents, Human-AI collaboration frameworks, and Synthetic Data generation.

- Strong proficiency in Python, R, SQL, TensorFlow, PyTorch, Scikit-learn, statistical modeling, cloud platforms, and MLOps frameworks.

- Demonstrated ability to influence senior leadership and drive AI adoption across multiple business functions.

- Excellent communication, presentation, and storytelling skills with the ability to explain complex AI concepts to technical and non-technical audiences.

- Strong business acumen with the ability to translate advanced AI capabilities into measurable business value.

- Proven leadership skills in managing cross-functional teams and solving complex business challenges.

Preferred Qualifications :

- Experience within Investment Banking, Financial Services, or Capital Markets organizations.

- Knowledge of AI governance, responsible AI, ethical AI frameworks, risk management, and regulatory compliance.

- Research publications, patents, or conference presentations in AI, Machine Learning, or NLP.

- Experience working in global, matrixed, and geographically distributed organizations.

- Recognition as an AI thought leader or industry expert.

Education :

- Master's degree in Computer Science, Statistics, Mathematics, Data Science, Artificial Intelligence, or a related Engineering discipline.

- Equivalent industry experience with relevant Data Science or AI certifications will also be considered.

- Strong programming expertise in Python, R, and SQL.