Lead Data Scientist

Job description

The Emerging Tech Standard Delivery Team drives the adoption of advanced technologies and develops enterprise‑ready solutions for D&A Operations. In this role, the individual partners with Operations and Technology teams to design data‑driven solutions that deliver clear business and customer value.  The position requires strong expertise in data analytics, NLP, deep learning, and data communication, along with the ability to learn financial content and core D&A business processes. The individual must stay current with emerging technologies—including Generative AI, Multimodal AI, LLMOps practices, AI Agents, and synthetic data techniques—and collaborate closely with operations groups and specialized machine‑learning teams to deliver scalable, innovative, and high‑impact solutions.  Role, Responsibilities & Key Accountabilities:  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  • Demonstrate strong expertise Generative AI and Multimodal AI advancements, including models that handle text, images, audio, and video in unified architectures, significantly reducing pipeline complexity  • 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.  • Apply advanced model evaluation, tuning, scaling, and continuous improvement cycles.  • Apply AI Agents and human‑AI collaboration frameworks, adopting AI as a productivity amplifier across business functions  • Understanding Synthetic Data generation techniques to overcome real‑data scarcity, enhance model robustness, and support privacy‑preserving AI 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  • Bring 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, guid