Senior Data Scientist

RCRTR Limited
Birmingham, United Kingdom
7 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior
Compensation
£ 117K

Job location

Birmingham, United Kingdom

Tech stack

Artificial Intelligence
Continuous Integration
Python
Machine Learning
Natural Language Processing
Unstructured Data
Large Language Models
Data Management
Data Pipelines
Data Generation

Job description

400 per day - inside IR35

Are you an expert in Natural Language Processing who thrives on building scalable, real-world AI solutions? We are seeking a hands-on Data Scientist to join a premier global credit ratings and financial information firm. You will be a key player in launching a brand-new, from-scratch analytics platform designed for elite institutional clients including corporate banks and asset managers.

The Opportunity

In this role, you will go beyond conventional boundaries to design, build, and deploy quantitative models that power advanced insights. You will collaborate with a cross-domain team of economists, political scientists, and developers to transform proprietary risk data into actionable strategic assets.

Your Impact

  • Model Innovation: Design and optimize risk models for analytics and generative AI applications using proprietary NLP data generation processes.
  • Pipeline Development: Develop and maintain robust ML and data pipelines for experimentation and deployment.
  • Insight Extraction: Prototype and test new approaches for extracting insights from structured and unstructured data.
  • Technical Translation: Explain ML/NLP model outputs and methodologies to non-technical stakeholders to drive strategic decisions.

Requirements

  • Core Technicals: High expertise in Python and Machine Learning (ML).
  • NLP Expertise: 3-5 years of experience in Natural Language Processing.
  • AI Knowledge: Familiarity with LangChain and LlamaIndex. The role involves using Large Language Models (LLMs) to build data models rather than building LLMs from scratch.
  • Deployment: Must understand the deployment process and CI/CD practices to troubleshoot, though a dedicated engineering team handles the heavy lifting.

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