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# Data Scientist - Machine Learning

- **Company:** Talenthawk Limited
- **Location:** Greater London, UK
- **Experience:** Experienced
- **Salary:** £100,000.0
- **Contract:** Permanent contract
- **Skills:** Training Data, Amazon Web Services, Amazon S3, Big Data, Statistical Hypothesis Testing, Python (Programming Language), Machine Learning, Delivery Pipeline, Information Technology, Data Pipelines
- **Published:** September 8, 2026
- **Apply:** https://www.collegerecruiter.com/job/2856365447-data-scientist-machine-learning

## About the Role

* Commercial Experience: 2-5 years in a quantitative or data science role. Focus on Machine learning during this period
 * Technical Proficiency: Strong mastery of Python and demonstrable experience deploying/monitoring models in an AWS production environment.
 * ML Expertise: Deep statistical understanding of machine learning techniques, specifically classification and optimisation techniques to manage trade-offs between related data points.
 * Analytical Mindset: Proven ability to surface features that drive decisions even when they are not directly observable from raw training data.
 * Communication: Ability to collaborate across technical and business functions, with the potential to grow into a client-facing capacity., * Education: Masters or Ph.D. in a highly quantitative field (Statistics, Financial Engineering, Computer Science, or Mathematics).
 * Industry Background: Any industry is considered but financial services would be a plus

## Description

In this role, you will lead the transition from a manual, prototype-based cleaning process to a fully automated, scalable Machine Learning pipeline. You will be responsible for identifying outliers within large-scale datasets, ensuring the accuracy of consensus pricing for financial derivatives, and building a system that learns and improves through a continuous human-in-the-loop feedback mechanism., * Model Design & Development: Design, build, train, and validate sophisticated ML models (including Random Forests and Boosted models) to automatically flag "bad" valuations across multiple dimensions.

 * Pipeline Automation (AWS): Build robust, production-ready data pipelines within the AWS ecosystem (S3, Lambda, etc.) to process high daily volumes of valuation data within tight windows.
 * Explain ability & Confidence: Develop methods to measure model confidence and provide clear reasoning for valuation decisions. You will ensure the system flags borderline cases for expert review to maintain high integrity.
 * Continuous Learning: Implement feedback loops where human corrections are automatically integrated into training data, allowing the model to evolve and improve accuracy over time.
 * Collaborative Innovation: Generate and test hypotheses to drive incremental progress, working closely with both technical teams and business stakeholders.


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