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# Senior Data Scientist - Conversational & Agentic AI

- **Company:** Swap
- **Location:** London, UK
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** A/B Testing, Artificial Intelligence, Google Analytics, Python (Programming Language), Machine Learning, Language Modeling, Performance Tuning, Recommender Systems, Tensorflow, SQL Databases, Workflow Management Systems, Google Cloud, Chatbots, Pytorch, Large Language Models, Multi-Agent Systems, Prompt Engineering, Scikit Learn, HuggingFace, Data Analytics, Xgboost, Machine Learning Operations, Virtual Agents
- **Published:** September 10, 2026
- **Apply:** https://www.collegerecruiter.com/job/2840587027-senior-data-scientist-conversational--agentic-ai

## About the Role

* 4+ years in data science specialising in NLP, conversational AI, and agentic systems
 * Expert experience designing chat flows, dialogue systems, autonomous AI agents, multi-agent architectures, and advanced prompt optimisation including few-shot learning, chain-of-thought reasoning, tool use, and comprehensive AI agent development and evaluation.
 * Advanced ML & LLMs: Proficiency in PyTorch, TensorFlow, Hugging Face, and fine-tuning techniques such as LoRA, QLoRA, or RLHF using Vertex AI Training and tools such as Axolotl or Unsloth
 * Data & Analytics: SQL experience, experimental design, and A/B testing, ideally with Google Analytics and Vertex AI
 * MLOps & Domain Knowledge: Production experience with Vertex AI Pipelines, MLflow, Weights & Biases or similar platforms. Advanced Python skills and experience with agent frameworks and orchestration tools.

## Description

* Agentic System Design & Multi-Agent Architecture: Design sophisticated AI agent ecosystems that autonomously handle complex e-commerce workflows.
 * Conversational Flow & Agent Orchestration: Develop chat flows that guide users through e-commerce discovery, advice, and purchase journeys while managing handoffs between specialised agents with natural conversation transitions.
 * Prompt Engineering & Agent Optimisation: Develop and optimise prompts for both conversational AI and autonomous agents, implementing few-shot learning patterns, chain-of-thought reasoning, tool use, and structured output generation for multi-step agentic workflows.
 * Fine-tune LLMs for Agentic Applications: Fine-tune language models for optimising both conversational flows and autonomous agent decision-making using techniques like LoRA, QLoRA, and full fine-tuning on e-commerce datasets with Vertex AI Training and frameworks such as Axolotl, Unsloth, or DeepSpeed.
 * Design Agentic Quality Metrics: Create robust metrics to track user engagement, task completion rates, agent performance, multi-agent coordination effectiveness, and business outcomes.
 * Build Predictive Models: Develop user behaviour prediction, conversation outcome forecasting, intent classification, agent performance prediction, and recommendation systems using scikit-learn, XGBoost, LightGBM, and Google Cloud AutoML.


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