Applied Data Scientist

Applied Analytics Engineer

Compensation: £70,000-£90,000 + 10% bonus (depending on experience)

Location:

Remote anywhere in the UK.

Models. Insights. Outcomes.

Become one of the changemakers. At Quid, youwon'tjust be joining a team, but contributing to a culture of innovation, where every challenge becomes an opportunity to learn and grow. When you join our team,you'renot just stepping into a job,you'are embracing a future where we lead the game with the unmatched advantage of foresight.

As an Applied Data Scientist at Quid, you will build data and AI-driven systems, integrate APIs, and support LLM-based agentic processes to create reliable and actionable data flows. You will also bring stronger experimentation and validation rigor to our delivery - designing baselines, running evaluation cycles, and building predictive and statistical models where they create clear customer value.

This role suits someone who enjoys end-to-end automation, collaborating with analytics and engineering teams, and turning ambiguous needs into scalable solutions. Your work will power key insights and operational outputs across professional services (known as our Outcome Engineering Team), enabling faster delivery, higher data quality, and AI-driven prototypes. Youwon'tjust build workflows
-you'llhelp shape the next evolution of our data and automation ecosystem and the intellectual property that underpins it.

• Build workflow automations in n8n, developing modular, reusable sub-workflows and scalable patterns, including structured outputs for Coda briefs and visualisation platforms.

• Integrate with internal and external APIs, handling authentication, error recovery, retries, rate limits, and tolerant connectivity patterns.

• Build and refine agentic workflows using LLMs, including guardrails, safe failure modes, and input validation, and experiment with emerging automation and AI frameworks to introduce new patterns and capabilities.

• Monitor and troubleshoot workflow executions across APIs, agentic behaviour, data transformations, and orchestration layers, implementing effective logging, alerting, and debugging strategies.

• Design and run validation studies and experiments (gold sets, baselines, metric selection, error analysis) to measure and improve workflow and model quality.

• Build and ope rationalise predictive and statistical models in Python where they create clear value, including evaluation plans and drift monitoring approaches.

• Break down ambiguous requests into scoped work packages, prototypes, and MVPs.

• Own workflows end to end, from concept to deployment to ongoing monitoring.

• Core languages:
  Strong Python skills for analysis, experimentation, and modelling. Basic JavaScript for writing expressions and transformations in n8n. Strong SQL skills (Postgre
  
  SQL preferred).

• Experience:
  
  2-3 years building automation, data pipelines, integration workflows, or applied analytics/data science solutions in production contexts.

• Predictive/statistical modelling:
  Experience building and evaluating machine learning models (e.g., regression/classification/time series approaches) and translating results into practical workflow decisions.

• Experimentation and validation:
  Experience defining baselines, selecting evaluation metrics, labeling/QA of ground truths, running iterative validation cycles to improve quality, and drift monitoring.

• Workflow automation:
  Hands-on experience with n8n (or comparable workflow automation tools), including modular workflow design and reusable patterns.

• API integration:
  Experience integrating APIs with robust error handling, authentication, rate limiting, and debugging.

• Visualisation:
  Ability to deliver structured outputs and support lightweight visualisation needs.

• Data handling:
  Ability to manipulate and validate structured datasets (JSON, CSV, YAML) with attention to data quality and schema consistency.

• Engineering foundations:
  Testing and QA practices, deployment workflows, documentation habits, modularisation, and coding best practices.

• Observability and reliability:
  Strong monitoring, logging, alerting, and troubleshooting capabilities for multi-step automation systems.

• Change management:
  Experience promoting workflows safely into production and managing production-impacting updates.

• Ways of working:
  Requirements gathering, comfort with ambiguity, iterative prototyping, and end-to-end workflow ownership.

• Communication:
  Ability to translate technical concepts, risks, and constraints into clear guidance for stakeholders.

• LLM ecosystem:
  Exposure to embeddings, vector stores, or retrieval-augmented generation (RAG) patterns.

• AI and agentic workflows:
  Experience building and maintaining LLM-based workflows with guardrails, hallucination mitigation, and safe failure patterns.

• Prompt engineering and LLM interaction design:
  Experience designing and maintaining production-grade prompts for LLM-driven systems, including clear instruction framing, structured and…