Technical Capabilities: Where Product Strategy Meets AI/ML Execution

I don't just define products—I can validate technical approaches, write proof-of-concept code, and speak fluently with data scientists, ML engineers, and architects. I'm the product leader who can debate gradient boosting parameters and explain the business case to the CFO.

The Unicorn: Product strategy + hands-on technical execution

What I Can Build For You

Organized by the business problems I solve

When You Need to Predict Customer Behavior

Build predictive models that forecast churn, conversion, lifetime value, and next-best actions

I can:

  • Design and validate churn prediction models that catch at-risk customers 60-90 days early
  • Build propensity scoring systems for next-product recommendations
  • Create behavioral segmentation using clustering algorithms (K-means, DBSCAN, hierarchical)
  • Develop lifetime value models that inform acquisition spend and retention strategies
Random Forest Gradient Boosting XGBoost Scikit-learn Feature Engineering Python

Real application: Built ML churn model at USAA that saved $12M by predicting dissatisfaction 60+ days before customers left

When You Need to Prevent Violations & Detect Anomalies

Build real-time monitoring systems that catch compliance issues and fraud before they become problems

I can:

  • Design configurable rules engines that compliance teams can adjust without engineering support
  • Build anomaly detection models that learn "normal" and flag deviations automatically
  • Create real-time transaction monitoring that catches violations at scale
  • Develop fraud scoring systems that balance false positives with risk tolerance
Anomaly Detection Isolation Forest Rules Engine Real-Time Processing Azure DevOps CI/CD

Real application: Led development of Control Tower/Sentry360 at ServiceMac—compliance platform with 5.2x ROI monitoring millions of transactions daily

When You Need to Optimize Operations & Reduce Costs

Build intelligent routing, automation, and resource allocation systems that do more with less

I can:

  • Design NLP-powered intent detection for smart call routing and prioritization
  • Build workflow automation that handles routine tasks while flagging edge cases for humans
  • Create predictive scheduling and resource allocation models
  • Develop optimization algorithms that balance efficiency with quality
NLP Amazon Connect Intent Classification Sentiment Analysis Process Automation

Real application: Deployed real-time NLP routing at LoanCare that lifted conversions 18pp and reduced handle time 22%

When You Need to Personalize Experiences at Scale

Build segmentation, recommendation, and targeting systems that deliver 1-to-1 experiences to millions

I can:

  • Design micro-segmentation systems (50-100+ segments) based on behavioral patterns
  • Build recommendation engines using collaborative filtering and content-based approaches
  • Create A/B testing frameworks that support hundreds of concurrent experiments
  • Develop dynamic personalization that adapts in real-time based on user behavior
K-means Clustering DBSCAN Collaborative Filtering A/B Testing Recommendation Systems

Real application: Co-designed AI segmentation engine at Amazon that created 87 micro-segments and generated $38M incremental revenue

What Makes Me Different

Why technical depth matters in product leadership

I Can Debug Your Models

When your data scientist says "the model isn't converging," I can look at the learning curves, check for data leakage, and suggest regularization approaches. I don't just nod and trust the experts—I validate their work.

I Write Proof-of-Concept Code

Before committing engineering resources, I prototype solutions in Python. This de-risks projects and proves feasibility before anyone writes production code. I've saved teams months by discovering technical constraints early.

I Speak Both Languages

I can explain gradient boosting to a data scientist and explain the business case for ML to a CFO. This translation ability is rare—and it's why cross-functional teams actually ship products instead of arguing about priorities.

I Know What Can Go Wrong

I've debugged models in production, dealt with data drift, handled concept drift, and explained to executives why accuracy dropped 10 points overnight. Experience with ML in production makes me better at designing systems that won't break.

How I Work

My process for taking products from concept to production

1

Discover

Customer interviews, data analysis, competitive research. I find the real problem—not the symptom everyone's complaining about.

2

Design

Prototype solutions, validate technical approach with data scientists, define success metrics. I write SQL and Python to test assumptions.

3

Build

Agile sprints with data scientists, ML engineers, and developers. I review code, debug models, and make trade-off decisions in real-time.

4

Measure

Track KPIs, run experiments, iterate based on data. I build dashboards and monitor model performance to ensure continuous improvement.

Technical Stack Reference

Tools and technologies I work with daily

AI/ML & LLMs

• Python, SQL
• LangChain, OpenAI, Anthropic APIs
• Scikit-learn, XGBoost, Pandas
• RAG Pipelines, Vector Databases
• Transformers, BERT, GPT models
• Feature Engineering

Data & Analytics

• SQL (Advanced queries)
• Snowflake, Databricks
• Tableau, PowerBI
• dbt (data transformations)
• Experiment Design & A/B Testing
• Statistical Analysis

MLOps & Infrastructure

• AWS (SageMaker, Lambda, Connect)
• Azure DevOps, CI/CD
• Git, GitHub
• Docker, Containerization
• Model Monitoring & Observability
• Airflow (workflow orchestration)

Product & Collaboration

• Agile/Scrum methodologies
• Jira, Azure Boards
• Figma (UX collaboration)
• Notion, Confluence
• Customer Discovery & User Research
• Roadmap Planning & OKRs

NLP & Text Analytics

• Amazon Connect, Lex
• Intent Classification
• Sentiment Analysis
• Named Entity Recognition
• Text Embeddings (BERT, Word2Vec)
• Topic Modeling

Financial Services Domain

• Mortgage Servicing Operations
• Regulatory Compliance (CFPB, RESPA)
• Credit Risk Modeling
• Fraud Detection Systems
• Customer Lifecycle Management
• Banking & Payment Systems

Need a Product Leader Who Can Actually Build AI Products?

Most AI PMs talk strategy. I can also validate architectures, write prototypes, and debug models. Let's discuss your technical challenges.