Hire a Freelance SciPy Developer
I'm a Python developer available for remote freelance work involving SciPy — signal processing, numerical optimisation (scipy.optimize), and statistical analysis (scipy.stats). Most of it ends up as the numerical layer inside a bigger pipeline: an AI agent that needs a confidence threshold, a data pipeline that needs an outlier filter, a reporting job that needs a real statistical test instead of an eyeballed chart.
Signal processing
Filtering, FFTs, and smoothing on time-series or sensor-style data using scipy.signal — cleaning noisy input before it reaches a model or a report.
Optimisation
scipy.optimize for constrained and unconstrained numerical optimisation — parameter fitting, cost minimisation, threshold tuning for scoring logic.
Statistical analysis
scipy.stats for hypothesis tests, distributions, and confidence intervals — the difference between "this metric moved" and "this metric moved and it's statistically real."
AI agent scoring logic
SciPy's statistical tools inside Claude-driven agent pipelines — confidence scoring, threshold-based escalation (e.g. routing from a cheap model to an expensive one only when uncertainty is high).
Data pipelines — pandas & numpy alongside SciPy
SciPy rarely runs alone — usually paired with pandas for the data wrangling and numpy for the array work underneath it. See the full Python stack →
Typed, tested, production-shaped
Function interfaces with type hints, not exploratory notebooks. Code that's meant to run unattended on a schedule or inside a pipeline, not re-run cell-by-cell by hand.
Real-browser SEO co-pilot: backlink qualifier, GSC insights, relevance scorer, cluster audit. The scoring and threshold logic behind the relevance scorer is the same statistical-confidence pattern SciPy work usually lands on. Fully open source.
github.com/dannwaneri/seo-agent →Python agent that runs Claude Haiku first, escalates to Sonnet only when confidence is below threshold. The escalation threshold itself is exactly the kind of numerical-optimisation problem SciPy is built for.
Read the freeCodeCamp writeup →51+ articles on freeCodeCamp and dev.to. Every project I ship gets a writeup.
Signal processing (filtering, FFTs), numerical optimisation (scipy.optimize), and statistical analysis (scipy.stats) — usually as the numerical layer inside a larger pipeline or AI agent, not as a standalone script.
Production. Typed function interfaces, no notebook-only deliverables — code that runs unattended in a pipeline or on a schedule.
Yes — most of my recent work pairs SciPy's statistical and optimisation tools with Claude-driven agents, using scipy.stats for confidence scoring and threshold logic inside agent decision pipelines.
Have a SciPy problem to solve?
Signal processing, optimisation, or statistical analysis — tell me what you're building and I'll tell you if it's a good fit.
Related services