Economics Data Scientist

Description & Requirements

Bloomberg has 70 economists and analysts and 200 economics and government reporters worldwide, with an outstanding capacity to explain where the world is economically and politically, where it might be heading, and the ways in which geopolitical and economic forces interact.

Bloomberg Economics offers a comprehensive macroeconomic research service for Terminal subscribers, including analysis of major data releases and economic events, detailed forecasts, geo-economic research, and sophisticated modeling. The goal is to offer Bloomberg clients a deeper insight into the themes that drive policy, financial markets and capital flows.

London

Research

• The successful candidate will work with Bloomberg’s economic modeling team to build and maintain machine-learning models, data pipelines, and other analytical tools to support research.
• You’ll help turn text and other unstructured datasets into usable economic and geopolitical signals.
• Work on models and tools for tasks such as sentiment analysis, signal extraction, classification, and index construction.
• Build and manage pipelines to organize data, automate analysis, and support regular publication of results.
• You’ll also have opportunities to contribute to high-priority research and analysis, either independently or in collaboration with colleagues.
• We’ll measure success by the quality of the analysis, the reliability of the models, the efficiency of development, and your overall contribution to the broader research agenda.

• Build robust Python workflows for data analysis, modeling, and research production.
• Support colleagues in developing models, indicators, and other analytical outputs.
• Work with structured and unstructured data, including news, text, market, and economic datasets.
• Evaluate models carefully and communicate trade-offs clearly with colleagues and stakeholders.
• Provide ad‑hoc analytical support to high‑priority research projects.

• 5+ years of experience in a role involving quantitative analysis, data science, and machine learning for economic and financial research.
• A degree in a quantitative subject.
• Experience building models, data pipelines, and other analytical tools using real-world datasets.
• Strong Python skills, including experience with data analysis and modeling libraries.
• A solid understanding of statistics, machine learning, and deep learning, with the skills needed to conduct model evaluation and interpret `black box` model results.
• Knowledge of SQL or a keen interest in learning.
• Strong communications skills in English.
• The energy and the initiative to advance simultaneous projects on tight deadlines.

• Experience applying cutting‑edge techniques in machine learning or NLP to economic, financial, news or geopolitical data.
• Working knowledge of machine learning, NLP and data engineering tools, with experience using frameworks such as scikit-learn, PyTorch, Hugging Face, spaCy, or similar technologies.
• Experience working with alternative, unstructured or large‑scale datasets.
• Experience building dashboards or visualisations with libraries such as Streamlit or Plotly.