# Louis Magowan · ML/AI Engineer & Social Scientist · Sydney

> Louis Magowan is a Sydney-based ML/AI engineer and social scientist who built Qonto's first AI analytics agent and has published education policy research.

Hi, I'm Louis

## ML/AI engineer _& social scientist._

I build secure, scalable and cost-efficient software that serves hundreds of thousands of customers. My academic background is in social science at LSE, with a particular interest in education research and policy.

[Get in touch](https://louis-magowan.com/#contact)

louis.py

```python
from dataclasses import dataclass


@dataclass
class Louis:
    role: str = "Freelance ML engineer"
    based_in: str = "Sydney, Australia"
    education: tuple = (
        "MSc Applied Social Data Science, LSE",
        "BSc Social Policy, LSE",
    )
    builds: tuple = (
        "Production AI agents",
        "ML services and APIs",
        "Data pipelines and platforms",
        "MLOps and observability tooling",
        "Web front ends, including this site",
    )
    published_in: tuple = (
        "Journal of Computational Social Science",
        "Towards Data Science",
        "The Qonto Way",
    )
    speaks: tuple = (
        "English (native)",
        "French (C1)",
    )
    interests: tuple = (
        "cooking",
        "travel",
        "tennis",
        "meditation",
    )


me = Louis()  # scroll for more ↓
```

### About

I'm half-Aussie, half-Irish and have lived between the two countries, with periods of work and study in Paris and London. I moved back to Sydney in 2025.

I've worked across the Data stack, as both a data analyst and a data scientist. Over time, though, I realised I'd rather be an explorer-builder than just the explorer that “Data” roles often require. I moved into ML and AI engineering and have had no regrets – it's been the most stimulating work of my career. I thrive in zero-to-one settings, with ambiguity and autonomy in equal measure, and I'm thrilled when I encounter a problem that can only be solved with outside-the-box thinking.

I'm currently freelancing as an ML, AI and software engineer, and taking some time to upskill. I've been brushing up on front-end development (this website was good practice) and learning Rust. I'm also looking for volunteering opportunities, especially in education, where I can use my professional skill-set for good, as I've done for charities before.

AI is one of the most disruptive technologies in history. I remain optimistic about its transformative potential but recognise the many risks it poses to modern society. I believe it can and must be used in a judicious, equitable manner and with the appropriate safeguards. It should be used to alleviate suffering and drudgery, but not more than that.

Art will always be a **_human_** endeavour.

#### Open to

- Freelance ML, AI and full-stack engineering projects
- Education and ed-tech work in Sydney
- Volunteering that uses data or AI for good

#### Right to work

- Australia · Australian citizen
- UK · British citizen
- EU · Irish citizen

### Experience

1. Jan 2023 – Jul 2026 · Paris, then remote from Sydney

   #### Qonto · European fintech for SMEs

   1. Staff Machine Learning Engineer · Feb – Jul 2026 · contract

      Designed and shipped Qonto's first AI analytics agent: a secure LLM-to-SQL agent for financial analysis, used by over 500,000 customers, with over 85% answer satisfaction, 99.9% uptime and P95 latency under five seconds. Delivered a new Python Accounting Engine as a FastAPI service, migrating legacy Go functionality and combining deterministic rules with LLM-generated ledger entries to raise document validity from ~30% to ~70%.
   2. Machine Learning Engineer · Jan 2025 – Feb 2026

      Built the engine that detects recurring transactions across every customer's account, powering the cash-flow management feature of the Qonto app. Deployed it as a weekly Kubernetes job with Kafka event publishing, Prometheus metrics and Sentry alerting.
   3. Senior Data Scientist · Jan 2024 – Jan 2025

      Co-built Qonto's first in-house Bayesian marketing mix models, which informed its annual advertising budgets. Became a top-10 contributor to PyMC-Marketing.
   4. Data Scientist · Jan 2023 – Jan 2024

      Ran geo-experiments using augmented synthetic controls, delivering causal, statistically significant savings of ~€2.2 million in marketing costs, and adding an estimated €56.5k in incremental customer lifetime value.
2. Sep 2021 – Sep 2022 · London

   #### MSc Applied Social Data Science, LSE

   Studied Applied Machine Learning, Deep Learning, Applied Regression Analysis, Computer Programming, Data for Data Scientists and Research Design.
3. Oct 2019 – Aug 2021 · London

   #### Data Analyst, Jellyfish

   Provided insights to win a pitch for Nike's global app marketing account. Delivered a creative and copy A/B testing framework for numerous clients, including eBay, Rakuten and BMW. Worked daily with Google Ads, Meta, Apple Search Ads, SA360 and Firebase data.
4. Sep 2016 – Jun 2019 · London

   #### BSc Social Policy, LSE

   Studied economics, social research methods and education policy. First-class dissertation on how school classifications and structures can impact academic outcomes in Northern Ireland.

### Research

Journal of Computational Social Science · vol. 6 · May 2023 · sole author · open access

#### [Centre Assessment Grades in 2020: A Natural Experiment for Investigating Bias in Teacher Judgements](https://doi.org/10.1007/s42001-023-00206-x)

Teacher judgements shape students' futures, from the move to secondary school to university applications. In 2020, COVID interruptions meant England's exams were cancelled and teachers awarded grades instead, creating a natural experiment. By training machine learning models on 2018–19 GCSE results to predict what 2020 students would likely have received in exams, the study estimates the treatment effects of teacher assessment by students' demographic and socio-economic characteristics.

No category of student was marked down: teachers graded all above their likely exam results. Over-prediction varied, though, and intersecting three characteristics widened the gap between categories up to 3.4 times.

- **GCSE results analysed**: 3.1M
- **Students across 2018–2020**: 349k
- **Presented at**: Royal Economic Society Annual Conference, Glasgow, 2023

- [Read the paper](https://doi.org/10.1007/s42001-023-00206-x)
- [Code](https://github.com/louismagowan/cag-equality)
- [Results dashboard](https://datastudio.google.com/reporting/7c49d7ca-ae1c-43cf-a8f7-8e70d969fbad)

UK Statistics Authority · Digital Economy Act 2017 · accredited 2022 – 2027 · no. 36983

#### Accredited Researcher, Office for National Statistics

In the UK, confidential government records about individuals, such as pupils' exam results, are shared for research under the Five Safes framework. Safe data is handled before researchers are involved: records are de-identified before they reach the secure environment. Here's how the other four safes applied to the paper above.

- ##### Safe People

  Every researcher must be accredited by the UK Statistics Authority. I passed the ONS Safe Researcher assessment on confidential data and statistical disclosure control, and joined its public register.
- ##### Safe Projects

  The project took six months and three rounds of proposals to approve, with reviews by ethics committees, the Department for Education, Ofqual and the Research Accreditation Panel.
- ##### Safe Settings

  I could only work with the data on-site, in the ONS SafeRoom in London. Nothing left the room without clearance.
- ##### Safe Outputs

  Every result passed a two-stage check by the ONS disclosure team, and none describes fewer than 100 students, so no pupil could be identified.

- [Public register](https://uksa.statisticsauthority.gov.uk/digitaleconomyact-research-statistics/better-useofdata-for-research-information-for-researchers/list-of-accredited-researchers-and-research-projects-under-the-research-strand-of-the-digital-economy-act)

### Writing & Talks

- [Jun 2024 — Incrementality Test Scheduling — The Qonto Way · Marketing Measurement series — A discussion of threats to validity in marketing experiments, and when to trade rigour for speed.](https://medium.com/qonto-way/marketing-measurement-series-incrementality-test-scheduling-f7525b713b4a)
- [Jun 2024 — Marketing Mix Modeling at Qonto, Part VI: MLOps — The Qonto Way · Marketing Measurement series — Versioning hand-supplied model inputs with DVC, and tracking priors, diagnostics and code versions in MLflow.](https://medium.com/qonto-way/marketing-measurement-series-marketing-mix-modeling-at-qonto-part-vi-7bb9805076ba)
- [May 2024 — Marketing Mix Modeling at Qonto, Part II: Adstock and Saturation — The Qonto Way · Marketing Measurement series — Carryover and diminishing returns in advertising, explained with an embedded interactive app.](https://medium.com/qonto-way/marketing-measurement-series-marketing-mix-modeling-at-qonto-82e82c995b39)
- [May 2024 — Marketing Mix Modeling at Qonto, Part I: Getting Started — The Qonto Way · Marketing Measurement series — Where marketing mix models fit alongside attribution and experiments, and what a team needs before building one in-house.](https://medium.com/qonto-way/marketing-measurement-series-marketing-mix-modeling-at-qonto-337b8af11471)
- [Apr 2024 — Qonto's Path to MMM with PyMC — Invited talk · PyMC Labs — How Qonto moved to in-house Bayesian marketing mix modelling, and the MLOps behind it.](https://www.linkedin.com/posts/louismagowan_qontos-path-to-mmm-with-pymc-activity-7180825311574413312-UsYo/)
- [Sep 2023 — How to Prioritize Projects and Scale Your Data Science Team Efficiently — The Qonto Way — Prioritising data science projects by their expected monetary impact, and using the same estimates to decide how fast to grow the team.](https://medium.com/qonto-way/how-to-prioritize-projects-and-scale-your-data-science-team-efficiently-d4694f22eb49)
- [Mar 2022 — Augmenting a Sales Pipeline with Google Analytics and Data Studio — Towards Data Science — Tagging pitch dashboards to learn which parts prospective clients actually read.](https://medium.com/data-science/augmenting-a-sales-pipeline-with-google-analytics-and-data-studio-c19e484c43fc)
- [Jan 2022 — Song Lyrics Over Time: Sentiment and Lexical Diversity Analysis in R — Towards Data Science · Featured article of the day · #1 trending in Sentiment Analysis — Billboard Top 100 lyrics since 1958 have become angrier and less lexically diverse.](https://medium.com/data-science/lyrics-analysis-5e1990070a4b)

### Skills

Adept at data science, machine learning and AI. Competent with back-end, some knowledge of front-end.

- Python
- TypeScript
- JavaScript
- HTML
- CSS
- R
- Bash
- scikit-learn
- TensorFlow
- Keras
- spaCy
- Optuna
- NumPy
- SciPy
- pandas
- Polars
- Jupyter
- Plotly
- Streamlit

- Claude
- Gemini
- MCP
- Pydantic
- FastAPI
- SQLAlchemy
- pytest
- uv
- Ruff
- Node.js
- npm
- Vite
- React
- Tailwind CSS
- Biome
- Vitest
- Chrome DevTools
- Git
- GitHub
- GitLab
- Dependabot

- Snowflake
- DuckDB
- PostgreSQL
- Kafka
- Airflow
- BigQuery
- Google Cloud
- Cloudflare
- Docker
- Kubernetes
- Helm
- Argo CD
- Terraform
- MLflow
- DVC
- Metabase
- OpenTelemetry
- Prometheus
- Grafana
- Sentry
- Jaeger

- **LLMs & Agents**: pydantic-ai, Guardrails, AWS Bedrock, Gemini on Vertex AI, Claude Code, MCP; agents with tools, LLM-to-SQL, prompt caching, LLM-as-a-judge evaluation, prompt-injection defence
- **ML & Statistics**: scikit-learn, LightGBM, Optuna, SHAP, TensorFlow/Keras, spaCy, PyMC, Prophet, statsmodels; Bayesian modelling, causal inference, A/B testing, forecasting, NLP, statistical disclosure control
- **Marketing Science**: PyMC-Marketing, CausalPy, Robyn, GeoLift; marketing mix models, geo-experiments with synthetic controls, conversion lift tests, SEO and generative engine optimisation (GEO); Google Ads, Meta Ads, SA360, Apple Search Ads, Google Analytics, Firebase
- **Back-End Engineering**: FastAPI, Pydantic, asyncio, PostgreSQL, Kafka, PASETO service auth, pytest, uv, ruff, mypy; API and database design
- **Front-End Engineering**: HTML, CSS, JavaScript, TypeScript, React, Tailwind CSS, Vite, Biome, Vitest, Playwright, Chrome DevTools; accessibility (WCAG 2.2) and progressive enhancement
- **Software Delivery**: Git, GitHub Actions, GitLab CI, Dependabot, LaunchDarkly, Lefthook, Make; trunk-based development, feature flags and progressive rollouts, AI-assisted code review
- **Data & Analytics**: SQL, regex, dbt, Elementary, Snowflake, BigQuery, DuckDB, Airflow, Polars, pandas; Metabase, Looker Studio, Tableau, Streamlit, Matplotlib, Seaborn
- **MLOps & Infrastructure**: MLflow, DVC, Docker, Kubernetes, Helm, Argo CD, Terraform, AWS, GCP, Cloudflare Workers
- **Observability**: OpenTelemetry, Prometheus, Grafana, Jaeger, Sentry, Finout; distributed tracing, structured logging, alerting, cost monitoring

### Open Source

- pymc-labs/pymc-marketing

  #### [PyMC-Marketing](https://github.com/pymc-labs/pymc-marketing)

  Historical top-10 contributor

  Created its first interactive MMM explainer app, plus Bayesian evaluation metrics and MLflow model registry support.
- pymc-labs/CausalPy

  #### [CausalPy](https://github.com/pymc-labs/CausalPy)

  Collaborator status

  Promoted to collaborator, the third of four levels on its [contributor pathway](https://github.com/pymc-labs/CausalPy/blob/main/CONTRIBUTING.md#contributor-pathways-and-permissions), after maintenance contributions to PyMC Labs' causal inference library.

### Volunteering

- #### French Red Cross

  Homelessness · 2023 – 2025

  ‘Maraudes’: offering food, tea and conversation to people living on the streets.
- #### Family Engagement Lab

  Education · 2020

  Built an analysis staff could rerun without knowing Python. The charity estimated it saved $6,100 and used it to guide its outreach.
- #### CrossPurpose

  Employment · 2020

  Modelled who benefits most from the charity's programme, and built a dashboard it extended to other areas of its work.

Contact

### Happy to talk about ML & AI, education, or using data for good.

[hello@louis-magowan.com](mailto:hello@louis-magowan.com) [LinkedIn](https://www.linkedin.com/in/louismagowan)