Full Stack Data Scientist

About the role

Full Stack Data Scientist

London

Company Overview

Model ML is the AI workflow builder transforming how major financial institutions produce and validate client-ready work. Model ML converts complex, manual processes into fully automated AI systems that scale across global teams. In under a year, Model ML has become one of the fastest-growing enterprise AI platforms worldwide and recently closed a $75 million Series A, one of the largest fintech Series A rounds ever. Investors include FT Partners, Y Combinator, LocalGlobe, QED, 13books, and others.

Role: Full Stack Data Scientist

This is not a standard BI or analytics role. You will own the entire data stack end-to-end: pipelines, models, analytics, monitoring, predictive insights, and the internal data products that power how the company operates.

You’ll build ETLs, productionise models, develop AI-friendly data structures, and ship insights that help us make product, customer, and operational decisions fast. You’ll work directly with founders, engineering, and GTM teams and be responsible for ensuring our data is accurate, reliable, real-time, and self-servable across the business.

This is a high-ownership, high-velocity role. If you enjoy building things from scratch, moving quickly, and turning messy problems into automated systems, you’ll thrive.

Key Responsibilities

1. Own the full data pipeline

- Build and maintain Python-based ETLs and API integrations into Redshift.

- Manage orchestration via GitHub Actions (currently no Airflow, no Fivetran).

- Ensure uptime, correctness, and monitoring across all pipelines.

2. Build and maintain the full analytics layer

- Build dbt models that are well-structured, documented, and agent-friendly.

- Maintain core business metrics, feature usage metrics, customer health signals, and financial KPIs.

- Create a clean, scalable semantic layer for LLMs to query reliably.

3. Predictive analytics & ML

- Ship lightweight but production-ready models: churn prediction, usage-based forecasting, anomaly detection, upsell signals, etc.

- Implement monitoring and automate downstream workflows using model outputs.

4. Self-serve data products

- Build dashboards in Looker that expose the right metrics to every team.

- Make analytics self-serve so anyone in the company can get answers fast.

- Set up proactive alerting when something breaks, spikes, drops, or trends.

5. AI-first development

- Use AI coding tools daily (Cursor, Codex, Claude Code, Windsurf) to increase velocity.

- Write clean, structured code that is easy for AI agents to navigate and edit.

- Contribute to internal tools and data products that power Model ML’s agents.

6. Cross-functional leadership

- Work directly with founders, engineering, product, and GTM.