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# Senior Data Scientist I

- **Company:** Hackajob Ltd
- **Location:** London, UK
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Text Processing, Python (Programming Language), Machine Learning, Natural Language Processing, Large Language Models, Multi-Agent Systems, Prompt Engineering, Model Validation, Generative AI, Information Technology, Virtual Agents, Software Coding
- **Published:** August 24, 2026
- **Apply:** https://www.totaljobs.com/job/senior-data-scientist/hackajob-ltd-job107890039

## About the Role

* Degree in a quantitative or technical field (Statistics, Computer Science, Mathematics, Data Science, etc.).
 * Strong experience in machine learning, NLP, and LLM-based modeling.
 * Strong experience designing and running experiments, including model evaluation and iteration.
 * Strong coding skills in Python.
 * Experience with generative AI techniques (e.g., prompt engineering, RAG).
 * Experience designing and evaluating hybrid search (semantic + lexical) using embeddings and vector databases.
 * Experience designing agentic workflows and reasoning strategies, with hands-on experience applying agent frameworks (e.g., Google ADK, LangChain, LangGraph, AutoGen) in real-world use cases.
 * Proficiency in data analysis tools.
 * Strong foundation in statistics, modeling, and large-scale text processing.

## Description

We are seeking a Senior Data Scientist I to help with the design and validation of Agentic AI-driven product capabilities within Protégé in PatentSight. This role focuses on defining what to build and why, leveraging machine learning, NLP, and large language models (LLMs) to solve complex Intellectual Property (IP) workflows. You will help drive experimentation, modeling, and evaluation, partnering closely with engineers to translate validated approaches into scalable, customer-facing solutions., * Develop and implement NLP, LLM, and generative AI approaches (e.g., RAG, prompt strategies, patent search).

 * Define agentic workflows and reasoning strategies for multi-step IP tasks.
 * Develop retrieval strategies, including hybrid search (semantic + lexical), and evaluation metrics (e.g., relevance, ranking quality).
 * Analyse large-scale IP datasets to extract insights and improve model performance.
 * Establish best practices for model evaluation, validation, and benchmarking.
 * Translate experimental results into clear product recommendations and business impact.
 * Collaborate with product, IP experts, and engineers to align solutions with user needs.


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