About me

Results-driven Staff/Lead Data Scientist and AI Engineer with 10+ years of experience designing and delivering end-to-end data science, machine learning, geospatial analytics, and AI solutions that solve complex business challenges and create measurable business value. Experienced in developing predictive models, recommendation systems, forecasting solutions, marketing analytics, causal inference, customer analytics, and connected vehicle intelligence using Python, SQL, Spark, Databricks, Azure, and cloud-based AI platforms. Proven ability to transform large-scale structured and unstructured data into actionable insights that improve operational efficiency, customer experience, and strategic decision-making.

Strong background in building production-ready machine learning pipelines, Generative AI applications, LLM-powered assistants, and geospatial analytics solutions while collaborating with engineering, product, marketing, and business stakeholders. Passionate about leveraging AI, advanced analytics, cloud technologies, and data engineering to develop scalable, high-impact products across automotive, retail, transportation, and technology industries, with a focus on delivering innovative, data-driven solutions from concept through deployment.

What i'm doing

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    AI & Machine Learning

    Develop and deploy machine learning models for classification, regression, forecasting, anomaly detection, recommendation systems, and optimization problems.Build scalable AI solutions using Python, SQL, Spark, Databricks, and cloud platforms to support enterprise decision-making.

  • Generative AI and LLM

    Generative AI & Large Language Models

    Design and develop LLM-powered applications, AI assistants, chatbots, and Retrieval-Augmented Generation (RAG) systems.Apply prompt engineering, model evaluation, NLP techniques, and AI orchestration frameworks to build intelligent automation solutions.

  • Data Science and Advanced Analytics

    Data Science & Advanced Analytics

    Transform complex datasets into actionable insights through predictive modeling, statistical analysis, experimentation, causal inference, and customer analytics.Develop forecasting models, segmentation strategies, and data-driven solutions to improve business performance and operational efficiency.

  • BI and Data viz

    Business Intelligence & Data Visualization

    Create executive dashboards, reporting solutions, and self-service analytics platforms using Tableau, Power BI, and other visualization tools.Translate analytical findings into clear business recommendations that support strategic planning and decision-making.

  • Geospatial and Location Intel

    Geospatial Analytics & Location Intelligence

    Apply GIS, spatial analytics, and machine learning techniques to solve location-based problems, including mobility analysis, risk modeling, clustering, and spatial prediction.Leverage geospatial technologies such as ArcGIS, GeoPandas, Shapely, and spatial databases to generate insights from geographic and connected data.

  • Data Eng. and Cloud

    Data Engineering & Cloud Analytics Platforms

    Design and implement scalable data pipelines, ETL/ELT workflows, and analytics platforms using cloud technologies, distributed computing frameworks, and modern data architectures.Build reliable data solutions that enable real-time analytics, machine learning operations (MLOps), and enterprise AI adoption.

Resume

Experience

  1. Senior Data Scientist @ Dominos

    2023 — 2026

    Developed GenAI-powered analytics solutions using Azure AI, OpenAI LLMs, OpenCV, Whisper, Prophet, and sentiment analysis to forecast event-driven demand and automate creative scoring, summarization, and campaign ranking, improving operational efficiency by 10%, increasing sales by 5%, and reducing manual review time by approximately 70%. Delivered marketing measurement and optimization insights through Marketing Mix Modeling, elasticity analysis, media attribution, Customer Lifetime Value, and Databricks Genie Space while leading collaboration with media agencies, vendors, and cross-functional stakeholders. Applied time-varying Bayesian hierarchical models, Difference-in-Differences, causal impact, A/B testing, and lift studies to measure incrementality and strengthen ROI decisions. Built SARIMA and gradient-boosting demand forecasts that improved accuracy by 15% and reduced inventory costs by 12%, and used GeoPandas and HDBSCAN to group similar DMAs, design localized geo-tests, and uncover geographic customer behavior patterns.

  2. Senior Data Scientist @ General Motors

    2013 — 2015

    At General Motors, developed enterprise-scale machine learning, data engineering, and geospatial analytics solutions using Python, SQL, Spark, Azure Databricks, Hadoop, RAPIDS cuSpatial, Dask, ArcGIS, and Power BI. Analyzed more than 50 billion connected-vehicle events and NHTSA crash data to build XGBoost, HDBSCAN, logistic regression, PCA, clustering, and federated-learning models for crash severity, road safety, and risky-driving detection, achieving a 10× lift for Usage-Based Insurance. Created map-matching, geo-indexing, crowdsensing, and road-condition detection solutions for Future Roads, contributing to an estimated $48 million annual opportunity, while generating insights into congestion, emissions, traction, and weather for Safety View. Led data engineering work that reduced geospatial processing time from three hours to 30 minutes and applied RAPIDS cuSpatial and Dask to renewable-energy charging data, contributing approximately $1–$2 million in annual profit. Also automated MAP/POI pipelines that achieved over 95% data quality and reduced costs by 15%, conducted customer segmentation and profiling to support retail and marketing strategies, developed OnStar telemetry dashboards for disaster response, mobility monitoring, and vehicle tracking, and mentored MIT graduate students while serving on a CDAO advisory board.

  3. Software Engineer / GIS Data Analyst

    2003 — 2010

    Worked as a Software Engineer and GIS Data Analyst with Imperial Marketing, AT&T, Snap-on/Nexiq Diagnostics, and EDS (now HP) supporting OnStar, developing enterprise applications and geospatial solutions using .NET, C#, Java, SQL, GIS technologies, and related data-processing tools.

Education

  1. Ph.D. in Technology — Geographic Information Systems

    2014 — 2020

    Eastern Michigan University Dissertation: Traffic Crash Prediction Utilizing Connected Vehicle Basic Safety Messages, Geospatial Analysis, and Machine Learning

  2. M.Sc in Civil Engineering — Transportation Engineering

    2007 — 2009

    Wayne State University

  3. M.Sc in Computer Science

    2000 — 2002

    University of Detroit Mercy

Patents

  1. U.S. Patent No. US 11866052

    “Distributed computing system for determining road surface traction capacity”

Certificates

  1. Transportation Networks and Smart Mobility: Methods and Solutions Certificate

    MIT

  2. ICME Fundamental of Data Science Certificate

    Stanford University

  3. Software Engineering Certificate,

    University of Michigan-Dearborn

My skills

  • Data Science & Advanced Analytics
    95%
  • Artificial Intelligence & Machine Learning
    80%
  • Data Engineering & Cloud Platforms
    50%
  • Geospatial Analytics & Location Intelligence
    90%

Portfolio

Blog

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