ZORIN OS STABLE PYTHON v3.12 DOCKER CONTAINERISED IFRS 17 ▲ COMPLIANT ML PIPELINES ▲ LIVE QUANTLIB INTEGRATED REACT / FLASK FULL-STACK ACTUARIAL IFoA PATHWAY STOCHASTIC MODELS ▲ RUNNING WANTQUANT GOLD ▲ VERIFIED ZORIN OS STABLE PYTHON v3.12 DOCKER CONTAINERISED IFRS 17 ▲ COMPLIANT ML PIPELINES ▲ LIVE QUANTLIB INTEGRATED REACT / FLASK FULL-STACK ACTUARIAL IFoA PATHWAY STOCHASTIC MODELS ▲ RUNNING WANTQUANT GOLD ▲ VERIFIED
System Status: Ready for Engagement

QUANT
SYSTEMS
ARCHITECT

Bridging the gap between financial mathematics and production-grade engineering. I build the infrastructure that automates risk, scales research, and moves institutions forward.

→ SEE THE SYSTEMS [ SYSTEM SPECS ]
kunta_quant_systems — architecture.py
$ kqs deploy --engine=risk_v2 --env=prod
▶ Initialising Docker containers...
▶ Connecting to PostgreSQL cluster...
▶ Scaling Monte Carlo nodes (x4)...
▶ Running 10k paths on IFRS 17 liability engine...
▶ Deploying React Frontend to production...
▶ Health check: PASS | Latency: 12ms
✔ System Live → https://api.kqs.tech/v1/risk
$ _
3+Production Systems Deployed
99.9%Pipeline Reliability
GoldWorldQuant Verified
0Manual Reconciliation Errors
Infrastructure Built

ARCHITECTURES

I build high-performance systems at the intersection of actuarial science and software engineering. No manual spreadsheets. No fragile research scripts.

[ 01 / RISK ]
ACTUARIAL INFRASTRUCTURE
IFRS 17 reserving engines, liability modelling pipelines, and automated regulatory reporting systems. Built to survive audits and scale across portfolios.
IFRS 17QuantLibDockerMonte CarloALM
[ 02 / QUANT ]
RESEARCH PIPELINES
Automated backtesting frameworks and statistical validation engines that turn alpha signals into production-grade deployment code.
System DesignBacktestingStatsmodelsAPI DesignCI/CD
[ 03 / FINTECH ]
FULL-STACK FINTECH
End-to-end applications for credit scoring, predatory lending detection, and financial workflow automation. From UI/UX to high-availability backends.
ReactFlaskPostgreSQLXGBoostRedux
// kunta_quant_systems.spec.py
 
class KuntaQuant:
  def __init__(self):
    self.identity = "Joy Njoroge"
    self.role = "Quant Systems Architect"
    self.msc = "Financial Engineering"
    self.verified = ["WorldQuant Gold", "BSc Actuarial"]
    self.os = "Zorin OS"
 
  def output_standard(self):
    return {
      "rigour": "actuarial",
      "scale": "production-grade",
      "method": "automation-first"
    }
The Builder

RELIABILITY OVER SPECULATION.

I specialize in building the unbreakable engines that financial institutions require. With a background in actuarial science and an MSc in Financial Engineering, I bridge the gap between mathematical theory and production code.

From IFRS 17 reserving engines at Jubilee Insurance to AI-driven risk platforms like LoanLens, I focus on systems that eliminate manual error and drive operational efficiency.

Kunta Quant Systems (KQS) exists to provide production-grade engineering for firms that can no longer rely on Excel-based research.

Python 3.12ReactFlask DockerPostgreSQLQuantLib Zorin OSStatsmodelsCI/CD
The Build Pipeline

PROCESS

I don't just write scripts; I architect deployments. Every project follows a rigorous engineering lifecycle.

01
DOMAIN MAPPING
Identifying regulatory constraints, mathematical requirements, and data lineage. Mapping the domain before the architecture begins.
02
SYSTEM PROTOTYPING
Building the core engine with full statistical validation. Every assumption is verified. No black boxes in the logic.
03
PRODUCTIONISING
Wrapping models in Docker containers, building robust APIs, and creating high-performance data pipelines.
04
DEPLOY & MONITOR
Live deployment with monitoring dashboards and full technical documentation for institutional handover.
// deployment_signal.exe

LET'S BUILD
THE SYSTEM.

Whether you need a custom risk dashboard, an automated reporting pipeline, or a production-grade fintech platform — let's connect.