Data Scientist Analytics, Modeling & Risk Insights

SADIYA TIJJANI

Norfolk, VA ***** Active U.S. Government Security Clearance

240-***-**** *****.****@*******.*** linkedin.com/in/Sadiya Tijjani DATA SCIENTIST APPLIED ANALYTICS & PREDICTIVE MODELING Data Scientist who transforms complex, high-volume data into decision-ready insights through rigorous analytics, machine learning, geospatial analytics, and climate-related risk analytics—delivering dependable models, clarity, and measurable value in regulated environments.

PROFESSIONAL SUMMARY

Data Scientist with direct experience applying statistical analysis, machine learning, natural language processing, geospatial analytics, and climate-related risk analytics to solve real-world problems in regulated, data-intensive environments. Demonstrated ability to analyze complex datasets, develop and evaluate predictive models, automate analytics workflows, and translate analytical findings into clear, decision-ready insights for technical and non-technical stakeholders. SELECTED ACHIEVEMENTS

• Built and evaluated supervised machine-learning models for healthcare risk stratification and fraud detection, achieving 95% and 85%+ predictive accuracy, and delivering decision-ready analytics.

• Developed and automated scalable analytics workflows on AWS, improving reliability, repeatability, and turnaround time for analysts and downstream users.

• Implemented NLP and retrieval-augmented generation (RAG) techniques to extract insights from large volumes of unstructured data, enabling faster analysis and more consistent, grounded outputs. CORE COMPETENCIES

• Applied Data Science & Analytics

• Predictive Modeling & Risk Analysis

• Statistical Analysis & Pattern Recognition

• Feature Engineering & Model Evaluation

• Natural Language Processing (NLP)

• Unstructured Data Analysis

• Analytics Workflow Automation

• Data-Driven Decision Support

• Visualization & Data Storytelling

• Cross-Functional Collaboration

• Responsible AI & Model Transparency

• Geospatial Analytics, and Climate-Related Risk

Analytics

TECHNICAL EXPERTISE

Programming & Data: Python • SQL • R • PySpark • PostgreSQL Machine Learning: Supervised & Unsupervised Learning • Classification & Regression • Model Optimization • Performance Metrics

NLP & GenAI: Text Analytics • Retrieval-Augmented Generation (RAG) Large-Scale Text Processing Cloud & Platforms: AWS (S3, EC2, RDS, SQS) Scalable Analytics Workflows Visualization & Reporting: Power BI • Dashboard Development • Exploratory Data Analysis (EDA) Practices & Governance: Reproducible Analytics • Model Monitoring • Bias Awareness • Documentation PROFESSIONAL EXPERIENCE

U.S. Department of Agriculture (USDA)

Large Data Analytics – Applied Data Science

• Strategically developed scalable data science and machine-learning workflows on AWS that converted complex datasets into dependable, decision-support outputs for operational and research stakeholders.

• Rigorously analyzed structured data using statistical techniques and feature engineering to surface meaningful patterns, risk indicators, and predictive signals in regulated environments.

• Systematically built and evaluated predictive models, documenting performance metrics to strengthen confidence, transparency, and downstream usability of analytic results.

• Deliberately automated recurring analytics workflows, improving efficiency, consistency, and turnaround time for analysts dependent on repeatable insights.

• Effectively collaborated with analysts and subject-matter experts to translate ambiguous problem statements into clearly defined analytic questions and model-ready solutions.

• Proactively applied responsible-AI and governance principles to reinforce model reliability, interpretability, and compliance expectations.

Rutgers University

Geospatial Data Analyst – Data Science & Analytics

• Thoroughly analyzed structured and unstructured datasets using Python and SQL to support applied modeling and spatial analytics initiatives with real-world relevance.

• Carefully prepared and refined data through cleaning, transformation, and exploration analysis to improve model readiness and analytical accuracy.

• Insightfully developed visualizations and analytic summaries that clarified complex findings for both technical and non-technical audiences.

• Intentionally contributed reusable analytics components and data workflows that reduced duplication and accelerated delivery across multiple efforts.

APPLIED ANALYTICS & MODELING EXPERIENCE

• Clinical Risk Stratification: Developed and evaluated supervised machine-learning models for mortality risk assessment, achieving ~95% predictive accuracy and supporting care-planning analysis.

• Cancer Early-Warning Analytics: Built predictive models identifying early-stage risk indicators, improving detection workflows and analytic efficiency.

• Fraud & Credit Risk Modeling: Implemented classification models using structured financial data, achieving >85% accuracy in identifying high-risk cases.

• NLP & GenAI (RAG): Implemented retrieval-augmented generation workflows to analyze large volumes of unstructured text, improving insight discovery and analyst efficiency. EDUCATION & CERTIFICATIONS

Ph.D., STEM (Data-Focused) – Rutgers University

AWS Solutions Architect – Master’s Level

Data Science & Machine Learning – Master’s Level

Contact this candidate