Sam Dcruze — Portfolio

Projekte

Zertifiziert

Selbstgesteuertes Projekt

Supply Chain Optimization with SAP Business AI

Business AI Associate · KI / ML · September 2026

88/ 100

Eine ProoV-Fallstudie · Lernprojekt, kein Arbeitsverhältnis

Built a credible supply-chain rescue plan for a fictional SAP manufacturer by identifying the true bottleneck, quantifying the production risk at €120,000 per idle day, and designing a Joule-style copilot that surfaces the single best next action from live operational signals. The plan includes explicit human-in-the-loop guardrails, a defensible €235,000 payback estimate, and a board-ready pilot ask with a clear success metric.

Bewertet nach

Problem framing & business sense30%
Business-AI solution design & guardrails30%
ROI and decision quality25%
Executive communication15%

Bestanden · Bestehensgrenze 60/100

Was herausstach6

Correctly ignored the decoy port delay and focused on the true constraint: the single-sourced critical component with ~2 weeks of cover.
Defined a practical copilot that reads live supplier health, inventory cover, open POs, and port status, then drafts a PO or reroute for human approval.
Set a clear never-do boundary: the system must not autonomously onboard an unqualified supplier or commit purchases without sign-off.
Delivered a tight board ask with scope, staffing, timeframe, and success metric: a 2-person, 60-day pilot targeting detection-to-mitigation under 1 day.
Leakage-Free Problem Diagnosis
Human-in-the-Loop Copilot Design

Meine eingereichte Arbeit19 Aufgaben

11 Aufgaben · 7.9k Zeichen · 8 Textantworten · 578 Wörter

SAP Brand Sentiment Pre181 Zeichen
Trust Pledge135 Zeichen
Diagnose Break300 Zeichen
Problem Statement1.0k Zeichen
Copilot Spec926 Zeichen
Guardrails Sort1.7k Zeichen
ROI Payback587 Zeichen
India Transfer629 Zeichen
Board Pitch1.6k Zeichen
Teach Back660 Zeichen
SAP Brand Sentiment Post188 Zeichen
SAP Brand Sentiment Pre14 Wörter
Trust Pledge20 Wörter
Diagnose Break23 Wörter
Problem Statement143 Wörter
Copilot Spec126 Wörter
Guardrails Sort127 Wörter
ROI Payback53 Wörter
India Transfer72 Wörter

Bewusst zusammengefasst — der eingereichte Code und die Texte bleiben privat, damit diese Seite nicht als Lösungsvorlage dient. Die vollständige Einreichung steht hinter dem verifizierten Zertifikat.

Verifiziertes ZertifikatFälschungssicher · ausgestellt von ProoV

Zertifiziert

Selbstgesteuertes Projekt

Machine Learning for Automotive

KI / ML · September 2026

84/ 100

Eine ProoV-Fallstudie · Lernprojekt, kein Arbeitsverhältnis

Built an end-to-end used-car pricing analysis for VW and Audi listings: combined the datasets, performed EDA on price, mileage, and brand differences, engineered predictive features, and compared Linear Regression with Random Forest using R² and MAE. The work culminated in a clear CPO-oriented recommendation to use the stronger model for standardized trade-in valuation and margin protection.

Bewertet nach

Exploratory Data Analysis20%
Feature Engineering25%
Model Building & Evaluation35%
Business Communication20%

Bestanden · Bestehensgrenze 60/100

Was herausstach6

Combined VW and Audi listings with a brand column and explored distributions, correlations, and brand-level price differences.
Identified the core pricing drivers in the business summary, including car age, mileage, and engine size.
Compared Linear Regression against Random Forest with explicit R² and MAE figures and tied the result to a CPO recommendation.
Exploratory Data Analysis
Feature Engineering
Model Comparison and Evaluation

Meine eingereichte Arbeit10 Aufgaben

5 Aufgaben · 6 Zeilen · 5 Textantworten · 141 Wörter

VW Sprint1 Complete56 Zeichen
VW Sprint2 Complete61 Zeichen
VW Sprint3 Complete59 Zeichen
VW Business Insights1.2k Zeichen
Jupyterlite Code2 Zeilen
VW Sprint1 Complete1 Wort
VW Sprint2 Complete1 Wort
VW Sprint3 Complete1 Wort
VW Business Insights137 Wörter
Jupyterlite Code1 Wort

Verifiziertes ZertifikatFälschungssicher · ausgestellt von ProoV

Zertifiziert

Selbstgesteuertes Projekt

Energy and Renewable: Machine Learning for the Power Grid

Junior Machine Learning Engineer · KI / ML · September 2026

92/ 100

Eine ProoV-Fallstudie · Lernprojekt, kein Arbeitsverhältnis

Built a leakage-aware gas-turbine TEY predictor using the eight pre-combustion sensors, with CO and NOx excluded because they are downstream emissions. Validated on a chronological holdout, compared Linear Regression against a Random Forest, and reported honest held-out performance (about 1.5 MWh MAE, ~0.98 R2) alongside residual analysis and a practical monitoring plan.

Bewertet nach

Build and validate the model correctly35%
Optimize and evaluate honestly25%
Write the Model Card (clarity + honesty)25%
Explain it to a non-expert15%

Bestanden · Bestehensgrenze 60/100

Was herausstach6

Correctly excluded CO and NOx as downstream leakage features and documented why
Used a time-based split instead of a random shuffle, preserving the integrity of the evaluation
Reported held-out MAE, RMSE, and R2, then read the residuals for bias and heteroscedasticity
Wrote a model card that names the plant-specific limits, silent-drift risk, and a concrete retraining trigger
Leakage-Free Feature Selection
Chronological Holdout Validation

Meine eingereichte Arbeit20 Aufgaben

12 Aufgaben · 13 Zeilen · Python, JavaScript · 8 Textantworten · 431 Wörter

Problem Frame238 Zeichen
Leakage Flags113 Zeichen
Train Validate BaselinePython · 1.4k Zeichen
Train Validate124 Zeichen
Train Validate Forest944 Zeichen
Tune Evaluate SetupPython · 1.5k Zeichen
Tune Evaluate ResidualsPython · 2 Zeilen
Tune Evaluate130 Zeichen
Residual Read256 Zeichen
Drift Monitor92 Zeichen
Model Card1.1k Zeichen
Teach BackJavaScript · 736 Zeichen
Problem Frame20 Wörter
Leakage Flags1 Wort
Train Validate Baseline141 Wörter
Train Validate1 Wort
Train Validate Forest77 Wörter
Tune Evaluate Setup110 Wörter
Tune Evaluate Residuals80 Wörter
Tune Evaluate1 Wort

Verifiziertes ZertifikatFälschungssicher · ausgestellt von ProoV

Zertifiziert

Selbstgesteuertes Projekt

Driving Supply Chain Sustainability via Data Governance

Product Compliance Specialist · Allgemein · September 2026

84/ 100

Eine ProoV-Fallstudie · Lernprojekt, kein Arbeitsverhältnis

Built a complete battery-passport compliance package for a single cell: classified regulated fields as known/compute/gap, designed public/legitimate-interest/restricted passport views, and traced every remaining gap to the supplier tier that owns it. Also computed the cell’s carbon footprint from code, identified raw materials as the dominant emissions source, and drafted actionable supplier requests to close the highest-priority data gaps.

Bewertet nach

Supply-chain gap attribution and supplier requests30%
Judgment under regulatory ambiguity20%
Access-tier design judgment15%
Carbon-footprint methodology and calculation15%
Passport data-model completeness and traceability10%
Internal consistency across the package10%

Bestanden · Bestehensgrenze 60/100

Was herausstach6

Computed the carbon footprint in code and reported the stage breakdown and total (74.05 kg CO2e/kWh) rather than asserting a number.
Correctly identified raw materials as the dominant emissions stage and drew the right operational conclusion about upstream sourcing leverage.
Built a coherent access-tier split that distinguishes public passport fields from recycler-useful legitimate-interest data and restricted due-diligence records.
Drafted supplier requests with named fields, regulatory basis, deadlines, and requested formats, making them actionable for real contacts.
Leakage-Free Regulatory Classification
Honest Model Evaluation

Meine eingereichte Arbeit16 Aufgaben

8 Aufgaben · 8.0k Zeichen · Python · 8 Textantworten · 582 Wörter

Passport Field Defences2.3k Zeichen
Passport Field Classification659 Zeichen
Carbon Footprint CalculationPython · 1.3k Zeichen
Access Tier Design918 Zeichen
Supply Chain Gap Audit316 Zeichen
Supplier Data Request1.3k Zeichen
Passport Verdict Reveal490 Zeichen
Final Submission809 Zeichen
Passport Field Defences160 Wörter
Passport Field Classification1 Wort
Carbon Footprint Calculation118 Wörter
Access Tier Design1 Wort
Supply Chain Gap Audit1 Wort
Supplier Data Request142 Wörter
Passport Verdict Reveal56 Wörter
Final Submission103 Wörter

Verifiziertes ZertifikatFälschungssicher · ausgestellt von ProoV

Zertifiziert

Selbstgesteuertes Projekt

Business Analytics Sprint

Data Science · September 2026

92/ 100

Eine ProoV-Fallstudie · Lernprojekt, kein Arbeitsverhältnis

Built an end-to-end profit-paradox analysis: framed hypotheses and KPI drivers, audited the data, wrote correct SQL for category and regional margin analysis, isolated the main profit drivers, and translated the findings into a board-ready recovery plan. The work is especially strong in connecting technical analysis to executive decisions, with a clear forecast and prioritized actions focused on margin repair.

Bewertet nach

Problem Framing & Hypotheses10%
Data Analysis & SQL30%
Root-Cause & Forecasting25%
Dashboard & Visualization15%
Executive Communication & Strategy20%

Bestanden · Bestehensgrenze 60/100

Was herausstach6

Wrote both SQL queries with the correct margin formula and appropriate GROUP BY/JOIN logic
Correctly identified the main profit drivers as raw-material inflation, logistics, and discounting
Produced a coherent forecast and tied it to prioritized recovery actions
Communicated the story clearly across memo, deck, stakeholder Q&A, and executive summary
Structured Hypothesis Formation
Leakage-Free SQL and Margin Analysis

Meine eingereichte Arbeit20 Aufgaben

12 Aufgaben · 5.9k Zeichen · 8 Textantworten · 301 Wörter

Hypothesis Sheet1.1k Zeichen
KPI Driver Map174 Zeichen
Data Audit100 Zeichen
Week1 Update693 Zeichen
Sql Category Profit354 Zeichen
Sql Region Join491 Zeichen
Root Causes158 Zeichen
Forecast276 Zeichen
AI Reflection617 Zeichen
Recommendations693 Zeichen
Boardroom Deck676 Zeichen
Stakeholder Qa550 Zeichen
Hypothesis Sheet129 Wörter
KPI Driver Map1 Wort
Data Audit4 Wörter
Week1 Update91 Wörter
Sql Category Profit27 Wörter
Sql Region Join38 Wörter
Root Causes5 Wörter
Forecast6 Wörter

Verifiziertes ZertifikatFälschungssicher · ausgestellt von ProoV