Senior Data Scientist

Benefits

Additional Information

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: Own the product vision for an industry‑leading data management framework, covering data acquisition, cleaning, transformation, and related workflows. Support Lead Data Scientist on end-to-end AI/ML lifecycle: problem definition, design, experimentation, deployment, and continuous improvement. Define an optimized user experience for financial analytics data pipelines, unifying multiple tools and services into an integrated workflow. Partner with analytics domain experts to understand business data needs and translate them into a practical platform and framework strategy. Lead the execution of this strategy in close collaboration with engineering and project delivery teams. Communicate product goals, progress, and achievements to product, engineering, business leaders, sales, proposition, support, and other internal stakeholders. Stay informed on financial analytics market trends to maintain a forward‑looking, future‑ready strategy. Continuously learn and adapt to emerging technologies and techniques in financial analytics. Develop and deploy production‑grade AI models and data‑driven solutions aligned with strategic objectives. Tune and optimize AI models to improve accuracy, performance, and scalability. Evaluate third‑party AI capabilities using both functional and non‑functional criteria. Establish and enforce coding standards to ensure a maintainable, robust codebase. Collaborate with Software Engineers and cross‑functional teams to define requirements, set direction, and shape development roadmaps. Hands‑on experience in data science, including Natural Language Processing, Large Language Models, prompt engineering, and Retrieval‑Augmented Generation (RAG). Skilled in training, testing, and validating deep learning models as well as classical machine learning models. Ability to design and implement evaluation frameworks for automation solutions based on business requirements. Experience building solutions and onboarding workloads on AWS or other cloud platforms. Expertise in web crawling and scraping techniques, entity identification and extraction, and advanced pre‑ and post‑processing methods using emerging technologies such as prompt engineering. Proficient in processing structured, semi‑structured, and unstructured data from PDFs and scanned documents. Required Skills: - 6-8 years of experience in data science, analytics, or statistical modelling roles. - Strong foundation in data analytics, feature engineering, NLP, predictive modelling, LLMs, and RAG workflows. - Expertise in Python and core ML/DL libraries (TensorFlow, PyTorch, Scikit‑learn), with demonstrated experience in developing and productionizing AI models. - Proven ability to apply strong statistical analysis skills, including probability and applied statistics, to model development and evaluation. - Experience with Git‑based version control and CI/CD pipelines (GitLab/GitHub runners). - Strong understanding of relational databases, large‑scale data processing, and statistical programming. - Familiarity with MLOps principles and experience deploying solutions on cloud platforms (AWS and/or Azure). - Hands‑on experience with commercial or open‑source data management tools. - Excellent communication and presentation skills, capable of simplifying complex technical concepts for both technical and business audiences. - Strong influencing and collaboration skills, supporting cross‑team execution and alignment. - Ability to diagnose and resolve complex technical and business problems using deep analytical and industry knowledge - Ability to set and uphold coding standards. - Hands-on experience with leading commercial or open-source data management tools. - Excellent written, verbal, and presentation skills, with the ability to present to peers, senior management, and cross-functional partners. - Broad business awareness and understanding of relevant internal business relationships. Preferred Experience in large financial services or investment banking environments. Familiarity with Refinitiv or LSEG products and financial data workflows. Experience working with global or distributed teams. Comfort interacting with senior stakeholders, including executives and C‑suite partners. Education: - Master's degree in Statistics, Mathematics