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  "basics": {
    "name": "Dareselam Mohammed",
    "label": "Data Analyst",
    "summary": "Data Analyst with a strong quantitative background and an academic trajectory toward Artificial Intelligence and Machine Learning. Experienced in data analysis, statistical methods, Python-based data processing, and data-driven problem solving. Interested in machine learning, graph/network analysis, and computational methods for complex datasets. Seeking a research internship at KAUST to develop practical research experience in graph machine learning and network analysis.",
    "profiles": [
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        "network": "LinkedIn",
        "username": "dareselam-mohammed-ab67732a3",
        "url": "https://linkedin.com/in/dareselam-mohammed-ab67732a3"
      }
    ]
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  "work": [
    {
      "company": "University of Messina, Dept. of Applied Statistics",
      "position": "Data Analyst / Research Assistant",
      "location": "Messina, Italy",
      "startDate": "2025-02",
      "endDate": "2025-06",
      "highlights": [
        "Processed and cleaned 50M+ rows of telematics and mobility data to support accident and risk prediction initiatives",
        "Developed predictive models (logistic regression, gradient boosting) to identify high-risk driving patterns, improving risk stratification accuracy by 15%",
        "Designed and maintained reproducible analysis notebooks and SQL queries used by the research and insurance-partner teams",
        "Produced clear visualizations and dashboards to communicate trends in driver behavior, exposure, and claim probability to non-technical stakeholders",
        "Co-authored internal reports summarizing methodology, model performance, and practical recommendations for pricing and risk-management teams"
      ]
    }
  ],
  "education": [
    {
      "institution": "University of Messina",
      "degree": "Bachelor of Science",
      "field": "Data Analysis",
      "score": "3.8/4.0",
      "highlights": [
        "Relevant coursework:\nStatistics and Probability\nLinear Algebra,\nProgramming(Python,C,Java)\nData Analysis,\nDatabase Systems,\nMachine Learning ,\nMathematics "
      ]
    },
    {
      "institution": "",
      "degree": ""
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  "skills": [
    "Python",
    "SQL",
    "Power BI",
    "Tableau",
    "Data Analysis",
    "Machine Learning",
    "Data Visualization",
    "ETL",
    "A/B Testing",
    "Regression",
    "Feature Engineering",
    "Stakeholder Communication",
    "Python (Pandas, NumPy, SciPy)",
    "SQL (PostgreSQL, MySQL)",
    "Excel/Google Sheets",
    "Exploratory data analysis (EDA)",
    "KPI design",
    "dashboarding (Power BI, Tableau)",
    "reporting automation",
    "classification",
    "time-series forecasting",
    "experiment design",
    "data quality checks",
    "data validation",
    "API-based data ingestion",
    "Telematics & mobility analytics",
    "risk modeling",
    "customer behavior analysis",
    "Git",
    "Jupyter",
    "Lead Student Developer & Coordinator",
    "Campus Event Coordinator & Peer Mentor"
  ],
  "languages": [
    {
      "language": "English",
      "fluency": "Fluent"
    },
    {
      "language": "Italian",
      "fluency": "Advanced"
    },
    {
      "language": "Amharic",
      "fluency": "Native"
    }
  ],
  "projects": [
    {
      "name": "Telematics Risk Scoring & Dashboard",
      "description": "Used a large driving behavior dataset (speed, acceleration, braking, time-of-day, distance driven) with several million rows. Cleaned and transformed the data, handling missing values, outliers, and inconsistent records. Engineered features such as speeding ratio, harsh braking per 100 km, night driving percentage, and average trip duration. Trained logistic regression and gradient boosting models to predict the probability of an accident in the next 30 days. Evaluated models using ROC-AUC, precision-recall, and calibration plots. Built a Power BI dashboard to visualize risk scores by driver, vehicle, region, and behavior patterns, including filters to simulate improvements in driving style. Documented the full workflow in Jupyter notebooks and a clear project README.",
      "keywords": [
        "Python",
        "SQL",
        "Power BI",
        "scikit-learn"
      ]
    },
    {
      "name": "Graph and Network Analysis with Python",
      "highlights": [
        "Represented real-world relationships as nodes and edges and analyzed network structure using Python.",
        "Calculated network measures such as degree, centrality, and community structure.",
        "Built reproducible analysis workflows using NetworkX and Pandas.",
        "Investigated how network characteristics change under different graph structures and assumptions."
      ],
      "keywords": [
        "Python",
        "NetworkX",
        "Pandas",
        "Matplotlib"
      ]
    },
    {
      "name": "AI-Assisted Study & Productivity Bot",
      "description": "Built a custom web app integrating the Claude API to summarize lecture notes, generate interactive practice quizzes, and answer student queries in real time.\n* Authored clean, modular code with robust API rate-limiting handling and published setup documentation on GitHub.",
      "keywords": [
        "Python",
        "Node.js",
        "Express",
        "JavaScript (ES6+)",
        "Anthropic Claude API",
        "HTML5/CSS3",
        "RESTful APIs",
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        "GitHub"
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      "Excel/Google Sheets",
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      "dashboarding (Power BI, Tableau)",
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      "University Tech Club",
      "Campus Event Coordinator & Peer Mentor",
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    "yearsOfExperience": 0,
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    "highestEducation": "Bachelor's"
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