Senior Data Scientist - Conversational & Agentic AI

Swap
City of London
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

Senior Data Scientist

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

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.

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

If you’re applying for data science roles in the UK, it’s crucial to understand what hiring managers focus on before they dive into your full CV. In competitive markets, recruiters and hiring managers often make their first decisions in the first 10–20 seconds of scanning an application — and in data science, there are specific signals they look for first. Data science isn’t just about coding or statistics — it’s about producing insights, shipping models, collaborating with teams, and solving real business problems. This guide helps you understand exactly what hiring managers look for first in data science applications — and how to structure your CV, portfolio and cover letter so you leap to the top of the shortlist.

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

Data science has become one of the most visible and sought-after careers in the UK technology market. From financial services and retail to healthcare, media, government and sport, organisations increasingly rely on data scientists to extract insight, guide decisions and build predictive models. Universities have responded quickly. Degrees in data science, analytics and artificial intelligence have expanded rapidly, and many computer science courses now include data-focused pathways. And yet, despite the volume of graduates entering the market, employers across the UK consistently report the same problem: Many data science candidates are not job-ready. Vacancies remain open. Hiring processes drag on. Candidates with impressive academic backgrounds fail interviews or struggle once hired. The issue is not intelligence or effort. It is a persistent skills gap between university education and real-world data science roles. This article explores that gap in depth: what universities teach well, what they often miss, why the gap exists, what employers actually want, and how jobseekers can bridge the divide to build successful careers in data science.

Data Science Jobs for Career Switchers in Their 30s, 40s & 50s (UK Reality Check)

Thinking about switching into data science in your 30s, 40s or 50s? You’re far from alone. Across the UK, businesses are investing in data science talent to turn data into insight, support better decisions and unlock competitive advantage. But with all the hype about machine learning, Python, AI and data unicorns, it can be hard to separate real opportunities from noise. This article gives you a practical, UK-focused reality check on data science careers for mid-life career switchers — what roles really exist, what skills employers really hire for, how long retraining typically takes, what UK recruiters actually look for and how to craft a compelling career pivot story. Whether you come from finance, marketing, operations, research, project management or another field entirely, there are meaningful pathways into data science — and age itself is not the barrier many people fear.