Associate Director, Data Scientist

As a Data Scientist at BMI in London, you will design, build and deploy quantitative models that power risk analytics for clients across sectors. You will collaborate with economists, analysts and developers to deliver interpretable, scalable solutions that inform strategic decisions. You’ll work with proprietary risk data, advance NLP-driven insights, and help evolve modeling frameworks to meet diverse customer needs in a fast-paced, cross-disciplinary environment. This role offers the chance to impact how global organizations manage country risks and opportunities.

• Hybrid Work Environment (3 days in office)
• Culture of Learning & Mobility
• Retirement planning and tuition reimbursement
• Comprehensive healthcare offerings
• Parental leave policy
• Volunteer days and matched donations

• Prototype and test new approaches to extract insights from structured and unstructured data for corporate clients
• Develop and maintain robust ML and data pipelines for experimentation and deployment
• Design, build, and optimize risk models for analytics and generative AI using the proprietary NLP data generation process
• Collaborate across Economists, Industry Analysts, Political Scientists and Developers
• Explain ML/NLP model outputs and methodologies to non-technical stakeholders

• Substantial experience querying, cleaning, compiling, and analysing big data
• Experience with data mining, data visualization, NLP, text analysis, and basic time series forecasting and ML models
• Experience with Python, R and libraries (e.g., numpy, pandas, scikit, pytorch, tidyverse, caret, ggplot)
• Proven experience developing, refining, and monitoring NLP models
• Familiarity with database and data access tools (e.g., SQL, S3, Sagemaker, API protocols) is preferred
• Understanding model evaluation methods and metrics
• Ability to operationalize non-technical ideas into research designs, features and outputs
• Familiarity with experiment tracking and model management tools (e.g., DVC, Weights & Biases)
• Experience with interpretable AI techniques

• Collaborative cross-functional communication
• Ability to translate technical outputs for non-technical stakeholders
• Adaptability in a fast-paced environment
• Python
• R
• NLP and text analysis