Data Science & Machine Learning
Turn data you already have into models, forecasts, and dashboards that drive decisions.
The problem
What this solves
- “Data sits unused across disconnected systems.”
- “No in-house ML capability to prototype ideas.”
- “Manual analysis can’t spot patterns or anomalies.”
- “Leadership wants forecasts, not spreadsheets.”
Sound familiar?
What we deliver
Tangible outputs
- Predictive models (churn, demand, risk, forecasting).
- Anomaly and fraud detection prototypes.
- Clean, reproducible data pipelines.
- Dashboards that turn models into decisions.
- An honest read on what your data can and can’t support.
Tech stack
Tools and platforms
Python
pandas
scikit-learn
PyTorch
XGBoost
SQL
AWS SageMaker
AWS Bedrock
Streamlit
Power BI
Process
How the work flows
01
Audit
Assess data quality, availability, and the real question.
02
Design
Frame the problem and define a measurable target.
03
Build
Prototype, evaluate honestly, and iterate.
04
Handover
Ship a usable prototype with documented assumptions.
Pricing model
Ways to engage
Data audit
Assess feasibility before you invest.
Prototype build
A working proof-of-concept model.
Monthly retainer
Iterate toward production ML.
Proof
This isn't theory
Financial services / Data science
3rd / 251
final private leaderboard
0.3631
private RMSE (log-space)
18M
transactions modelled
3rd of 251: forecasting bank transaction volumes in the Nedbank ML Challenge
Read the case studyReady to talk?
Book a free 15-minute intro call. We'll scope whether this is the right fit. No pressure.