specinsure
A Python toolkit for simulating specialty insurance losses and the reinsurance structures that protect them, using transparent Monte Carlo methods.
- Python
- NumPy
- SciPy
- Monte Carlo
Actuarial Analytics Engineer · The Fidelis Partnership
Goabaone Sehurutshe
I build the data products, pipelines and analytics tools that pricing, reserving and underwriting teams in the London specialty insurance market rely on, combining actuarial science with modern software and data engineering.
What I do
Actuarial data products
Trusted, tested datasets for pricing analytics, reserving and portfolio management, with validation and reconciliation built in.
Data engineering
ETL pipelines in Python, SQL and PySpark on Microsoft Fabric, plus FastAPI services that put analytics straight into underwriters' workflows.
Pricing & portfolio analytics
Rate change monitoring, exposure management, pricing models on Hx Renew, and Power BI / Plotly dashboards for underwriting and executive teams.
- IFoA exams passed
- 7
- Specialty lines supported
- 5
- MSc dissertation (A)
- 82%
Recent roles
Full experience-
Apr 2026 to Present
Actuarial Analytics Engineer
The Fidelis Partnership · London
Develop and maintain actuarial data products that provide trusted datasets for Pricing Analytics, Reserving and portfolio management teams.
-
Jan 2024 to Apr 2026
Portfolio Analytics Analyst
The Fidelis Partnership · London
Delivered analytics for pricing teams and underwriters across Marine, Aviation, Space, Political Risk and Structured Credit.
Open source
All projectsloss-modelling
Claims development triangles, chain ladder projections and Mack reserve uncertainty in plain, readable Python, validated against published benchmarks.
- Python
- pandas
- Chain Ladder
- Mack
insurance-data-pipeline
Map, validate and standardise insurance bordereaux: canonical schemas, per-source column mappings, row level issue reports and synthetic test data.
- Python
- pandas
- Data Quality
- Bordereaux
Latest writing
All writingBuilding Analytics Services for Actuaries and Underwriters With FastAPI
Why actuarial logic belongs in a service, how to structure one with FastAPI and Pydantic, test it properly, version it, and put it in...
Working With Microsoft Fabric for Actuarial Data: Lessons From Practice
Lakehouse or warehouse, medallion layers, idempotent PySpark merges, Delta table maintenance, Git integration and capacity: practical lessons for running actuarial pipelines on Microsoft Fabric....
Data Modelling for Insurance Analytics: Grain, Stars and Time
A dimensional model for premium and claims, why transaction facts beat snapshots of totals, slowly changing dimensions, and how to build reserving triangles from...
Let's talk
Happy to chat about actuarial analytics, data engineering in insurance, or opportunities in the London market.
Get in touch