Lead Data Scientist

• The Emerging Tech Standard Delivery Team drives the adoption of advanced technologies and develops enterpriseready solutions for D&A Operations.
• In this role, the individual partners with Operations and Technology teams to design datadriven 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 and collaborate closely with operations groups and specialized machine learning teams to deliver scalable, highimpact solutions.

Role, Responsibilities & Key Accountabilities:

Strategic & Technical Leadership

• Lead, mentor, and develop a team of senior data scientists, providing technical direction, coaching, and performance feedback.
• Drive endtoend delivery of data science initiatives, offering handson technical leadership, removing roadblocks, and ensuring highquality execution.
• Own and refine the product vision for an advanced data management and analytics framework, covering data acquisition, transformation, quality, and workflow automation.
• Define and optimize user experiences for financial analytics data pipelines, integrating multiple tools, data sources, and services into cohesive workflows.

Business & Stakeholder Engagement

• Partner with domain experts and senior business stakeholders to identify highvalue problems and shape actionable AI, ML, and platform strategies.
• Translate business needs into technical specifications, solution designs, and clear success metrics.
• Communicate project goals, insights, and outcomes to product, engineering, sales, proposition, support, and leadership stakeholders.
• Influence crossfunctional alignment by providing clear, datadriven recommendations and technical guidance.

Advanced AI/ML Delivery

• Develop, deploy, and optimize productiongrade AI models including deep learning, NLP, LLMs, and RetrievalAugmented Generation (RAG).
• Evaluate thirdparty AI technologies, frameworks, and tools to guide buildvsbuy decisions that enhance platform capabilities.
• Establish and enforce coding standards, quality controls, and reproducibility practices for robust ML development.
• Lead model performance tuning, scalability improvements, and continuous enhancement cycles.

Data Engineering & Processing Expertise

• Apply deep expertise in data extraction techniques, including web scraping, crawling, entity recognition, and advanced pre/postprocessing.
• Manage complex structured, semistructured, and unstructured dataincluding financial
• documents, PDFs, and scanned inputs.
• Collaborate with data engineering teams to ensure scalable and reliable data pipelines that support highimpact analytical workflows.

Cloud, MLOps & Deployment Leadership

• Ensure strong alignment with MLOps workflows, CI/CD pipelines, and cloudnative deployment practices.
• Lead scalable deployment of AI/ML solutions on AWS, Azure, or equivalent cloud environments.
• Work closely with platform engineering teams to enhance monitoring, observability, and model lifecycle management.

Continuous Improvement & Innovation

• Stay current with emerging technologies and trends in financial analytics, AI, NLP, cloud computing, and model engineering.
• Promote a culture of innovation by encouraging experimentation, exploration of frontier techniques, and continuous skill development across the team.
• Identify opportunities to improve frameworks, modeling practices, tools, and processes across the data science organization.

Required Skills:

• 8-12 years of experience in data science or related analytics and statistical modelling roles.
• Own and drive endtoend AI/ML solutionsfrom problem definition and experimentation to production deployment and continuous optimization
• Define and lead technical strategy, including model selection, evaluation metrics, and tradeoffs across performance, scalability, and constraints
• Design and deliver advanced AI solutions (including NLP, LLMs, GenAI, and RAG) aligned with business outcomes
• Act as the primary technical authority, guiding data scientists and ML engineers while proactively identifying and resolving risks
• Establish and sign off on success criteria, including model evaluation metrics, quality benchmarks, and production readiness standards
• Ensure Responsible AI compliance, including governance, explainability, documentation, and audit readiness
• Collaborate with engineering teams to enable secure, scalable, and reliable production deployments using MLOps and CI/CD best practices
• Lead and mentor senior data scientists, driving fullstack data science delivery and fostering strong problemsolving and algorithmic thinking
• Apply deep technical expertise in Python, ML/DL frameworks (TensorFlow, PyTorch, Scikitlearn), and largescale data processing
• Leverage strong foundations in statistics, data engineering, and cloud platforms (Azure/AWS) for production-grade solutions
• Translate business requirements into technical solutions and influence crossfunctional stakeholders to drive strategic outcomes

Preferred

• Experience working within large investment banking or financial services organizations.
• Experience defining enterprise AI governance, ethics guidelines, risk frameworks, or regulatory compliance programs.
• Published research, patents, or conference presentations in AI, ML, NLP, or related fields.
• Experience leading multiteam or matrixed delivery organizations across regions and time zones.
• Industry reputation as a technical expert or AI thought leader.

Education:

• Masters degree in Statistics, Mathematics, or Computer Science with a Data Science certification, or an Engineering degree specializing in Data Science and Artificial Intelligence.
• Proficiency in programming languages such as Python, R, and SQL.