Mohammed Rashad
certs
Gen AI Leader Learning PathGoogle CloudMachine Learning for BusinessDataCampSupervised Learning with scikit-learnDataCampWorking with Geospatial Data in PythonDataCampDAX Functions in Power BIDataCampSQL for Data ScienceUC DavisPython for Spreadsheet UsersDataCampJoining Data in SQLDataCampIntroduction to Statistics in Google SheetsDataCampIntermediate SQLDataCampIntroduction to SQLDataCampAccounting Job SimulationKoch IndustriesExcel Skills for BusinessGoldman SachsIntroduction to PythonDataCampIntermediate PythonDataCampIntroduction to Data Science in PythonDataCampExcel for AccountingLinkedInDigital Marketing FoundationsLinkedInThe Complete Digital Marketing CourseUdemyEnglish Proficiency, Grade 9Trinity College LondonGen AI Leader Learning PathGoogle CloudMachine Learning for BusinessDataCampSupervised Learning with scikit-learnDataCampWorking with Geospatial Data in PythonDataCampDAX Functions in Power BIDataCampSQL for Data ScienceUC DavisPython for Spreadsheet UsersDataCampJoining Data in SQLDataCampIntroduction to Statistics in Google SheetsDataCampIntermediate SQLDataCampIntroduction to SQLDataCampAccounting Job SimulationKoch IndustriesExcel Skills for BusinessGoldman SachsIntroduction to PythonDataCampIntermediate PythonDataCampIntroduction to Data Science in PythonDataCampExcel for AccountingLinkedInDigital Marketing FoundationsLinkedInThe Complete Digital Marketing CourseUdemyEnglish Proficiency, Grade 9Trinity College London

01 · selected work

One throughline: the data makes the call.

Filter by domain. Each card has the problem, the approach, and the numbers that mattered.

filter ·11 / 11
FinanceValuationDCFWACC

M&A Investment Proposal: Netflix ⇢ Paramount

problem · Determine strategic viability and fair value of a $30B+ media acquisition to expand content infrastructure.

implied
$18.58
bid
$14.49
market
$13.16
  • Aggregated 5 yrs of 10-K / 10-Q data to baseline revenue stability and debt obligations.
  • Built a dynamic 5-year forecast model; normalized non-recurring items → 9.27% projected revenue growth.
  • Ran DCF + sensitivity on WACC & Terminal Value, identifying 41% undervaluation ($18.58 implied vs $13.16 market).
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DerivativesRiskFinanceOptions

Quantitative Portfolio Management: StockTrak Simulation

problem · Generate alpha against S&P 500 while managing systemic risk during a high-volatility election cycle.

  • Built a high-alpha options strategy on MicroStrategy and Broadcom that delivered a 2.72 Sharpe ratio.
  • Used RSI / MACD technical signals to time NVDA & TSLA entries; weekly rebalancing at portfolio Beta 0.59.
  • Managed $1M active equity + derivatives book; 1st place finish, outperformed S&P 500 by 145% in 6 weeks.
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SQLDashboardsFinanceJoins & CTEs

Investment Portfolio Analytics & SQL Data Pipeline

problem · Consolidate fragmented high-net-worth asset data into a single source of truth for real-time risk monitoring.

equities
fixed-income
alts
cash
  • Engineered a relational pipeline using Joins, CTEs, and Views to aggregate disparate asset classes.
  • Computed Sigma (volatility) and risk-adjusted return to benchmark asset performance.
  • Shipped a dynamic dashboard (12M / 24M returns) and a risk-bubble chart to isolate underperformers for rebalancing.
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SQLLabor EconomicsAzure MySQLWage Modeling

U.S. Labor Market & H-1B Sponsorship Analysis

problem · Quantify real purchasing power of STEM roles across U.S. cities to optimize job search ROI for international graduates.

Austin
Atlanta
Boston
SF
NYC
  • Processed 500K+ FY2024 LCAs on Azure MySQL to identify high-probability STEM sponsors (Amazon, Infosys, etc.).
  • Engineered a cost-of-living wage model that flipped the ranking, showing Austin and Atlanta beat traditional tech hubs on real wage.
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PythonMLPandasSeaborn

Telecom Customer Churn Prediction

problem · Diagnose root causes of customer attrition and build predictive logic to trigger retention interventions.

at-risk cohort

4+ service calls

retention window

  • Python EDA (Pandas / Seaborn) surfaced a critical churn threshold, with users making 4+ service calls exiting at far higher rates.
  • Proposed a feature-bundle retention play for International Plan users, targeting the highest-risk segment.
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StatsOpsHypothesis TestingR

Statistical Analysis of MBTA Transit Efficiency

problem · Evaluate service reliability gaps between transit lines to propose operational efficiency improvements.

  • Hypothesis testing (N = 80+) proved the Orange Line consistently outperformed the Green Line despite higher demand.
  • Recommended real-time frequency modulation to mitigate bottlenecks, backed by crowd-vs-delay correlation.
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OptimizationLinear ProgrammingOpsExcel Solver

Logistics Network Optimization: Lavazza Coffee

problem · Redesign a global coffee producer's distribution network by siting a second warehouse to minimize annual transport and handling costs across 17,600 tonnes of demand.

L1
L2
L3
L4
L5
L6
  • Built a linear programming model in Excel Solver minimizing LTL + FTL + handling costs across 6 candidate warehouse locations vs. a central-only baseline.
  • Modeled the 40% FTL discount on inter-warehouse transfers and per-tonne LTL rates across 6 distribution-center customers; solved 7 scenarios with sensitivity reports.
  • Identified Location 4 as optimal: total annual cost dropped from €10.10M to €8.55M, a €1.55M (~15.4%) reduction.
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Power BIBIComplianceDAX

Workforce BI: JW Marriott San Antonio

problem · Bridge two disconnected workforce systems (WhenToWork scheduling + Kronos punch clock) at a luxury hotel to detect compliance risk before payroll close.

  • Built a 5-page Power BI dashboard plus an executive narrative on a star-schema data model spanning 8.5 months and 18 bi-weekly pay periods.
  • Implemented DAX measures for 7-minute punch rounding, weekly overtime (Sunday to Saturday, 40-hour threshold), missed-punch detection, and night/marathon-shift anomalies.
  • Built compliance trackers for the 19-hour weekly cap and 1,000-hour cumulative-tenure ceiling, flagging part-time student workers approaching benefit-eligibility thresholds before a breach.
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Power BIBIMarketingMySQL

Olist Sales Funnel Insights (Brazilian E-commerce)

problem · Help a Brazilian e-commerce marketplace cut client acquisition cost while protecting merchant revenue as post-pandemic tailwinds reversed.

Paid
Direct
Social
Display
Email
  • Built a Power BI dashboard from a MySQL extract of 8,000 leads and 842 converted merchants, mapping a 10.5% overall conversion rate across acquisition channels.
  • Surfaced Paid Search (12.3%) and Direct Traffic (11.2%) as the strongest channels vs Social (5.6%) and Email (3.0%); recommended reallocating 30% of underperforming spend to lift conversion to 13%.
  • Quantified revenue concentration risk: construction tools generated R$50.7M (~80% of merchant revenue), with manufacturers outperforming resellers 5 to 1.
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OptimizationPythonPower BIOR-Tools

RH Furniture: Route Optimization + BI Pipeline

problem · Cut delivery cost and CO₂ for a furniture retailer by solving the daily Vehicle Routing Problem and feeding optimized routes into a live Power BI dashboard.

  • Built a Python pipeline using Google OR-Tools to solve a capacity-constrained Vehicle Routing Problem on customer orders pulled from SharePoint Excel.
  • Engineered a bin-packing scheduler with time-window feasibility, splitting oversized orders across multiple delivery nodes.
  • Computed cost and CO₂ for each route vs. a naive baseline and exported 4 CSVs powering a single-refresh Power BI dashboard.
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NLPText MiningRTF-IDF

Airbnb Listings: Text Mining & NLP

problem · Decode why some Airbnb listings outperform others by mining 3,985 listings across 12 markets and isolating which content signals (not pricing) drive guest satisfaction.

  • Ran TF-IDF, bigram extraction, and Bing-lexicon sentiment scoring across listing descriptions and reviews to surface the vocabulary that separates top-rated from low-rated stays.
  • Showed that content quality, not tone, drives review scores: high-rated listings used markedly different vocabulary while sentiment polarity was nearly flat across rating groups.
  • Compared 12 markets and room types side by side; entire-home listings carried both higher prices and stronger review consistency.
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play with the math

Watch a route optimizer solve.

Drop delivery stops and hit solve. The tangled path untangles into the shortest route in real time, nearest neighbor first, then 2-opt swaps, the same idea behind my RH Furniture routing project. The savings is measured against the naive order.

vehicle routing · nearest neighbor + 2-opt

ready
depot

stops

11

naive route

2254

current route

2254

saved

0%

click the grid to add a stop