Senior Data Scientist - Conversational & Agentic AI

Swap
City of London
3 weeks ago
Create job alert
Senior Data Scientist - Conversational & Agentic AI

We are seeking a Senior Data Scientist to develop machine learning models and analytics systems that power intelligent conversational AI and autonomous AI agents for e-commerce. You will focus on model development, fine-tuning, measurement frameworks, and advanced ML techniques that enhance both conversational experiences and agentic capabilities.


Responsibilities

  • 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.

Skills & Qualifications

  • 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.

Why Join Us?

  • A truly global team. Swap operates across time zones, markets, and currencies.
  • Work with modern tech (AI-powered systems, cross-border APIs, advanced analytics).
  • Autonomy with high impact. Your decisions will shape the backbone of ecommerce infrastructure for years to come.
  • Equity, ownership, and career growth. We\'re scaling fast, and we want you on the journey.

Location

London, England, United Kingdom



#J-18808-Ljbffr

Related Jobs

View all jobs

Senior Data Scientist

Senior Data Scientist (GenAI)

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many AI Tools Do You Need to Know to Get an AI Job?

If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes. Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes. So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional. This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds. This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy. Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews. At the heart of this disconnect lies a growing and uncomfortable truth: Universities are not fully preparing graduates for real-world AI jobs. This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.