Data Scientist

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 and collaborate closely with operations groups and specialized machine learning teams to deliver scalable, high‑impact solutions. Role, Responsibilities & Key Accountabilities: Contribute to the development of an enterprise‑grade data management framework, including data acquisition, cleaning, transformation, and workflow optimization. Help design and refine user experiences for financial analytics data pipelines, integrating multiple tools and services into coherent, efficient workflows. Work closely with analytics domain experts to understand business data requirements and translate them into platform components, models, or analytical workflows. Collaborate with engineering and project delivery teams to implement data solutions that align with product and organizational goals. Support communication of project progress, insights, and outcomes to product, engineering, and business stakeholders. Stay current with emerging technologies, modelling techniques, and market trends in financial analytics to inform continual improvement. Build, train, test, and validate machine learning and deep learning models, including NLP, LLMs, and Retrieval‑Augmented Generation (RAG). Develop evaluation frameworks and metrics to assess automation and AI solutions against business requirements. Apply web scraping, crawling, entity extraction, and advanced pre‑/post‑processing techniques to prepare structured, semi‑structured, and unstructured data (including PDFs and scanned documents). Build and deploy data and ML solutions on cloud platforms (AWS or similar), following MLOps and CI/CD best practices. Required Skills: 3–5 years of experience in data science, analytics, or statistical modelling roles. Solid grounding in data analytics, feature engineering, predictive modelling, NLP, LLMs, and RAG workflows. Strong proficiency in Python and commonly used data science libraries (Pandas, NumPy, Scikit‑learn, etc.), with hands‑on model development experience. Familiarity with deep learning frameworks such as TensorFlow or PyTorch. Experience with Git and CI/CD pipelines (GitLab/GitHub runners). Strong understanding of statistics, relational databases, and statistical programming concepts. Ability to work effectively with large and complex datasets. Understanding of MLOps practices and experience with cloud environments (AWS/Azure). Experience with commercial or open‑source data management tools.