Python • Machine Learning • Chatbots • Automation

Python Programmer & Machine Learning Engineer

I build practical, production-minded systems that turn messy real-world needs into reliable automation. I created the first chatbot for the Amazon SOC, and I’m currently building my first startup — a mobile app in the publishing process.

Strengths: operational automation + ML-driven workflows Focus: chatbots + security/SOC processes Builder mindset: ship fast, iterate, own outcomes

Summary

Generated with Python (PyScript)

What I’ve Done

A concise overview of the work I’m known for.

Built the first chatbot for the Amazon SOC

Designed and delivered a chatbot to support Security Operations Center workflows, helping streamline common requests and reduce repetitive analyst effort. Built with a focus on reliability, operational clarity, and real-world usability.

Python Chatbots Automation Security Operations

Building my first startup (mobile app publishing)

Founder-building end-to-end: shaping product direction, implementing core features, tightening UX loops, and preparing for release. Shipping with fast iteration and a “make it work, then make it great” mindset.

Startup Mobile App Product Execution Iteration

Applied ML for automation

Build ML components when they add clear value—classification, routing, and then wrap them in clean, testable Python services.

Machine Learning NLP Python

Production-minded engineering

I prefer simple, maintainable systems: clear interfaces, strong logging, sane error handling, and a path for monitoring once the tool is in real hands.

APIs Reliability Operational UX

Builder + owner

Comfortable owning the full loop—problem definition, implementation, feedback, and iteration— and making tradeoffs that keep momentum without sacrificing quality.

Ownership Execution Iteration

Skills

Rendered by Python.

Experience Summary

Summary

Amazon SOC — Chatbot / Automation Work Key achievement
  • Created the first chatbot for the Amazon SOC, focused on streamlining SOC workflows and reducing repetitive analyst effort.
  • Built for operational reality: clear behaviors, predictable responses, and reliability under real usage patterns.
  • Approached automation with a security mindset: consistency, guardrails, and practical usability over hype.
Startup Founder — Mobile App (Publishing) Current
  • Building and publishing a mobile app startup, owning end-to-end delivery (product + engineering + iteration).
  • Shipping quickly with strong fundamentals: stable core features, fast feedback loops, and a path to scale.
  • Using Python and ML where it clearly improves the product and user experience.

Contact

Fastest way to reach me is LinkedIn.

Let’s connect.

If you’re hiring for Python/ML, chatbot automation, or building an MVP, send a message and include what you’re trying to ship.

Message me on LinkedIn

from pyscript import document PROFILE = { "name": "Jennifer Reevey", "headline": "Python Programmer & Machine Learning Engineer", "signature_1": "Built the first chatbot for the Amazon SOC", "signature_2": "Founder building a mobile app in the publishing process", "focus": ["Chatbots", "Automation", "Applied ML"], "style": ["Production-minded", "Operational clarity", "Fast iteration"], } SKILLS = { "Core Engineering": ["Python", "Automation", "APIs", "Testing", "Linux", "Logging/Observability"], "Machine Learning": ["Applied ML", "Evaluation", "NLP workflows", "Feature thinking", "Practical deployment"], "How I Work": ["Own outcomes", "Ship & iterate", "Keep systems maintainable", "Focus on real users"], } def stat_card(title: str, value: str, subtitle: str) -> str: return f"""
{title}
{value}
{subtitle}
""" impact = [ stat_card("Signature Work", PROFILE["signature_1"], "Chatbot + automation designed for real operations"), stat_card("Now", PROFILE["signature_2"], "Founder-mode: build, validate, publish"), stat_card("Focus Areas", " / ".join(PROFILE["focus"]), "Where I deliver the most leverage"), stat_card("Working Style", " • ".join(PROFILE["style"]), "Practical execution with strong fundamentals"), ] document.getElementById("impactStats").innerHTML = "\n".join(impact) blocks = [] for category, items in SKILLS.items(): tags = "".join([f'{i}' for i in items]) blocks.append(f"""
{category}
{tags}
""") document.getElementById("skillsBreakdown").innerHTML = "\n".join(blocks)