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, you will partner with Operations and Technology teams to design data‑driven solutions that deliver clear business and customer value.
Responsibilities:
- 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.
- 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.
- Familiarity with deep learning frameworks such as TensorFlow or PyTorch.
- Experience with Git and CI/CD pipelines.
- 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.
- Experience with commercial or open‑source data management tools.
- Strong analytical, problem‑solving, and prioritization skills.
- Effective communication skills with the ability to clearly articulate technical and conceptual ideas.
- Ability to set and uphold coding standards.
- Ability to collaborate across teams and contribute to strategy execution through strong technical insight.
Preferred Skills:
- Experience working in financial services or investment banking.
- Familiarity with Refinitiv or LSEG datasets, tools, or workflows.
- Experience working with distributed or cross‑location teams.
Education:
- Master’s 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 Python, R, and SQL