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JobDropper Consulting

Daniel Negrila-Mezei

Senior Credit Risk Modelling Consultant
Bucharest, Romania •Hybrid •Senior
not disclosed
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Senior Credit Risk Modelling Consultant
Overview
Senior Credit Risk Modeler and Consultant with hands-on experience in Basel/FRS9, PD/LGD/EAD and rating model development, validation and deployment.
CompanyJobDropper Consulting
LocationBucharest, Romania
Work typeHybrid
LevelSenior
Compensationnot disclosed
DomainCredit Risk

Proof items

Available now
Details
Professional profile Senior credit risk modeler and consultant focused on IFRS9, rating models and risk parameter estimation. Combines quantitative methodology development, SAS programming and .NET/C# tool development to deliver end-to-end modelling, validation and production solutions for banks and financial institutions. Core expertise - IFRS9 PD, LGD and EAD modelling and validation (including forward-looking components and stage allocation) - Rating model development (corporate, SME/PI) and transition/migration approaches - Statistical methods: OLS/LTS, quantile regression, Bayesian approaches, Vasicek one-factor, Markov-chain PD estimation - Robust estimation and outlier handling (Least Trimmed Squares, bootstrap) - Production-ready implementation: SAS (Base, Macro, STAT, ETS, IML, EG, Miner) and .NET/C# UI applications Experience evidence - Owner, JobDropper Consulting S.R.L. (since Nov 2022): delivered risk parameters estiamtion/validation methodologies and SAS solutions for Credex, Unicredit Bank(rating & LGD validation), Unicredit Leasing and Finance, Raiffeisen (IFRS9, collection scorecard) etc., support for BDO, Marsh, EY Romania. - PwC Romania (Oct 2020 – Nov 2022), Senior Manager FSRR: lead quant and SAS programmer delivering rating models, IFRS9 parameter modelling (LGD/EAD OLS and LTS, FL adjustment), and stage-2 allocation (quantile regression) for UniCredit Group and others. - KPMG Romania (May 2015 – Sep 2020), Manager Governance Risk & Reporting: main quant and SAS developer with group-level methodology input for UniCredit entities and IFRS9/rating model work. - ING Bank (2013–May 2015), Senior Credit Risk Modeling: developed models, coordinated data collection and trained staff in SAS and modelling. - BCR-Erste Bank (2008–2013): SAS programmer / Senior Statistician for risk parameters, provisioning, IRB input and real estate modelling validated by the National Bank of Romania. - Earlier roles include Raiffeisen Bank and data-science work for P2P lending (Lend Rise USA). Tools and methods - SAS suite: Base, Macro, SQL, STAT, ETS, OR, IML, EG, Graph, Miner - Programming and apps: Visual Studio / .NET, C#, C++, JavaScript, HTML/CSS - Statistical methods: OLS, LTS, quantile regression, Bayesian methods, Vasicek, Markov chains, bootstrapping, copula simulation etc. Education and certifications - University of Bucharest, Faculty of Mathematics (1994–1998), License: Theory and Construction of Real Numbers Career preferences - Preferred work setup: Hybrid - Focus areas: IFRS9, credit risk modelling (PD/LGD/EAD), rating models, model validation and consultancy projects - Open to consultancy and technical methodology roles supporting banks or financial institutions.
Gallery
10/104:49
High
Final score is the technical test score, completed in 4:49.
Expert / senior technical level
Card visibility
Visibility health
How strong this card looks for feed, search and browsing discovery.
7.4/10 Medium
Calculated from relevance, content quality, media proof, role fit, market benchmark and momentum. It is a discovery signal only; the final professional score stays driven by the technical test.
Fit 9.5/10
Core market signals are explicit: role, company, level, work type
Quality 9.0/10
Structured content is solid with 7 key fields completed
Media 8.9/10
Media stack includes cover, video and 8 extra assets
Search relevance 7.2/10
Content quality 9.0/10
Media proof 8.9/10
Role fit 9.5/10
Market benchmark 6.3/10
Profile freshness 3.0/10
Professional evidence check
Evidence strength + technical screening
Checks whether the description, domain signals and attachments support the declared role. The professional test remains the main validation signal.
ADVANCED PROFESSIONAL SIGNAL 87/100
The card strongly supports the Credit Risk field declaration with deep technical methods, extensive workflow engagement from modelling to deployment, and senior-level complexity and ownership in banking environments. Absence of attachments limits external validation scoring. Overall, the profile is consistent with a senior credit risk modelling and consultancy professional.
Credit Risk · Credit Risk Modelling and Validation (IFRS9, PD/LGD/EAD, Rating Models) · Senior
Analysis source: OpenAI
Evaluated: 12 Oct 2026 10:38. Responsibility evidence: zero means not demonstrated in the evaluated material, not zero ability.
Domain alignment95/100
Technical depth90/100
Workflow85/100
Responsibility evidence80/100
Attachments0/100
Validation50/100
Domain alignment: Strong alignment with declared Credit Risk field based on detailed modelling, validation and regulatory framework experience.
Model logic: Domain-aware evidence model for Finance / Credit Risk: Alignment 32% · Technical depth 22% · Workflow 16% · Complexity 12% · Attachments 6% · Validation 12%. EvidenceBase 79/100 × DomainConsistencyFactor 1.00 = Final professional evidence score 87/100.
Evidence read: The profile details extensive experience in credit risk parameter modelling, statistical methods, SAS programming, and end-to-end project delivery for major banks.
Declared vs detected: Credit Risk · Credit Risk
Weights: Alignment 32% · Technical depth 22% · Workflow 16% · Complexity 12% · Attachments 6% · Validation 12%
Level rationale: The evidence shows senior-level responsibilities including model development, lead quant roles, client consultation, and methodology ownership supporting large financial institutions.
Test result: 10/10 · 4:49 · Expert / senior technical level · from 1 attempt