Senior Data Scientist - Production ML & Analytics

Sheldon Gordon

Senior Data Scientist Applied Analytics & Production AI

Atlanta, GA 407-***-**** **************@*****.*** linkedin.com/in/sheldongordon4 github.com/sheldongordon4 PROFESSIONAL SUMMARY

Senior Data Scientist with 13+ years of experience applying statistical modeling, experimentation, and machine learning to support decisions in operational systems, including energy and manufacturing. Delivered 25%+ efficiency gains through anomaly detection, predictive analytics, and ML systems. Expert in problem framing, defining success metrics, and translating analytical results into clear, business-relevant insights, with experience deploying models reliably into production environments. CORE COMPETENCIES

• Machine Learning: Predictive Maintenance, Anomaly Detection, Statistical Modeling, Time-Series Analysis

• Production ML & MLOps: Model Deployment, FastAPI, Docker, CI/CD, Model Monitoring, Performance Optimization

• Operational Analytics: A/B Testing, Hypothesis Testing, Statistical Process Control, Risk Modeling, KPI Design

• Business & Communication: Technical Strategy, Executive Communication, Cross-Functional Collaboration

• Tech Stack: Python, SQL, PySpark, Git, AWS, Azure, MLflow, ETL Pipelines PROFESSIONAL EXPERIENCE

OEE IntelliSuite (Manufacturing Analytics SaaS) La Crosse, WI, USA (Remote) Senior Data Scientist (Contract) May 2025 – Present

• Led end-to-end development and deployment of predictive maintenance and reliability models, increasing asset uptime by 25% using gradient boosting and isolation forest techniques.

• Mentored and directed a 10-member cross-functional team through complex modeling challenges, aligning modeling priorities with operational KPIs and ROI-driven business objectives.

• Built analytics workflows processing 3M+ daily sensor events, supporting real-time anomaly detection and faster operational decision-making.

• Deployed production ML services using FastAPI and Docker with MLOps workflows, supporting scalable inference, versioning, and performance monitoring.

• Developed automated Python and SQL data pipelines for KPI validation, aggregation, and reporting, reducing manual analysis time by 35%.

• Migrated and standardized 10 analytics dashboards to Power BI, delivering real-time KPI visibility and executive decision-making. Energy Division – Ministry of Energy, Jamaica Kingston, Jamaica Risk Analytics Engineer Jun 2012 – May 2025

• Led national analytics initiatives supporting operational oversight and risk-based decision making through statistical analysis, across 300+ facilities in safety-critical sectors.

• Designed and maintained structured analytical datasets (PostgreSQL, Azure) from large-scale inspection and operational data, improving regulatory adherence by 70%.

• Reduced recurring safety violations by 55% through the development of predictive risk indicators that allowed for early intervention in high-risk operational environments.

• Architected risk and compliance analytics frameworks enabling consistent scoring, prioritization, and enforcement strategies.

• Translated complex analytical findings into actionable technical strategies for engineers, regulators, and senior leadership.

• Co-authored Jamaica’s first National AI Governance Framework, defining standards for model documentation, auditability, and risk controls.

ADDITIONAL EXPERIENCE

University of Technology, Jamaica Kingston, Jamaica (Remote) AI/ML Specialist (Part-Time) Sep 2025 – Present

• Collaborated with energy-sector stakeholders to scope and prioritize AI/ML use cases for grid modernization, defining KPIs and success criteria tied to operational outcomes.

• Engineered a multi-agent fault detection system for electrical grid diagnostics, combining ML-based anomaly detection with RAG-based SOP retrieval (ChromaDB, OpenAI embeddings), reducing fault resolution time by 40%.

• Built production-ready grid analytics services using FastAPI, Docker, and Streamlit to deliver real-time operational insights.

• Designed and delivered curriculum covering applied analytics, machine learning, deep learning, and MLOps, mentoring students on production-ready solutions.

TECHNICAL SKILLS

• Programming & Data: Python (Pandas, NumPy), SQL, PySpark, Git, Databricks

• AI & ML: Scikit-Learn, PyTorch, TensorFlow, LLMs, RAG, Prompt Engineering

• MLOps & Deployment: FastAPI, Docker, MLflow, CI/CD (GitHub Actions), Model Versioning & Monitoring

• Visualization & Reporting: Power BI, Tableau, Streamlit

• Cloud Platforms: AWS (S3, Lambda, SageMaker), Microsoft Azure (Data Lake, ML Studio) EDUCATION

Eastern University St. Davids, PA, USA

Master of Science in Data Science (Applied Machine Learning) – GPA: 3.97/4.0 University of Technology, Jamaica Kingston, Jamaica Bachelor of Engineering in Chemical Engineering (Process Optimization) CERTIFICATIONS

Microsoft Azure Fundamentals (AZ-900) AWS Certified Cloud Practitioner DeepLearning.AI Agentic AI Google Project Management Professional Certificate

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