JobDropper Consulting
Daniel Negrila-Mezei
Senior Credit Risk Modelling Consultant
Bucharest, Romania
•Hybrid
•Senior
Overview
Senior credit risk modeller and consultant with hands-on experience since 2000 in IFRS9, PD/LGD/EAD and rating model development, validation and deployment. Strong SAS programming expertise and experience building .NET/C# UI tools for model development. Delivered methodologies and implementations for banks and consulting firms (PwC, KPMG, ING, BCR, Raiffeisen) and runs JobDropper Consulting.
CompanyJobDropper Consulting
LocationBucharest, Romania
Work typeHybrid
LevelSenior
Compensationnot disclosed
DomainCredit Risk
Details
Overview
- Professional profile
- Senior credit risk modeller 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.
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.
7.5/10
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
9.6/10
Core market signals are explicit: role, company, level, work type
Quality
9.2/10
Structured content is solid with 7 key fields completed
Media
7.0/10
Media stack includes both cover and video
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.
SOLID PROFESSIONAL FIT
74/100
The extracted professional signal suggests: Finance / Credit Risk · Credit Risk Modeling / IFRS 9 · Solid mid-to-senior evidence.
Analysis source: Deterministic evidence engine
Alignment92/100
Technical depth76/100
Workflow66/100
Complexity86/100
Attachments69/100
Validation26/100
Domain alignment: Strong alignment: declared field 'Finance / Credit Risk' is supported by content evidence (pd, lgd, ead, ifrs9, provisioning, scorecard).
Model logic: Domain-aware evidence model for Finance / Credit Risk: Alignment 32% · Technical depth 22% · Workflow 16% · Complexity 12% · Attachments 6% · Validation 12%. EvidenceBase 74/100 × DomainConsistencyFactor 1.00 = Final professional evidence score 74/100.
Evidence read: The content indicates Finance / Credit Risk / Credit Risk Modeling / IFRS 9, with concrete signals around pd, lgd, ead, ifrs9, provisioning, scorecard, validation. Attachments considered: 2.
Declared vs detected: Finance / Credit Risk · Finance / Credit Risk
Weights: Alignment 32% · Technical depth 22% · Workflow 16% · Complexity 12% · Attachments 6% · Validation 12%
Level rationale: Solid mid-to-senior evidence was assigned because the current content contains 6 technical terms, 3 applied procedures/workflows, 5 seniority or responsibility signals and 1 validation/result signals; score 74/100.
Test result: 10/10 · 4:49 · Expert / senior technical level · from 1 attempt
Recognized evidence
- Score formula: The active profile is 'Credit Risk evidence rubric' for Finance / Credit Risk. The model changes weights by domain: regulated or technical fields emphasize alignment, technical depth and workflow evidence, while creative/commercial fields give more weight to portfolio or results. Attachments support the score but do not override missing domain evidence. Current calculation: EvidenceBase 74/100 × DomainConsistencyFactor 1.00 = 74/100.
- Domain alignment: Strong alignment: declared field 'Finance / Credit Risk' is supported by content evidence (pd, lgd, ead, ifrs9, provisioning, scorecard).
- Evidence depth: description is detailed (2759 chars) and gives enough context for evaluation.
- Detected content domain: Finance / Credit Risk is inferred from the current description/attachments.
- Specialization detected: Credit Risk Modeling / IFRS 9 is supported by wording in this card.
- Field-specific technical signal: declared-field evidence detected: pd, lgd, ead, ifrs9, provisioning, scorecard.
- Practical evidence: detected validation, provisioning, scorecard.
- Seniority evidence: detected governance, validation, lead, senior, owner.
Issues / risks / missing proof
Attachments
- Domain alignment score: 92/100 (weight 32%).
- Field-specific technical depth: 76/100 (weight 22%).
- Applied workflow/procedure evidence: 66/100 (weight 16%).
- Professional complexity/seniority: 86/100 (weight 12%).
- Attachment support: 69/100 (weight 6%).
- Validation/results evidence: 26/100 (weight 12%).
- ARIMA.pdf (306 KB): Professional document format; content should be reviewed for technical depth.
- Calibrating the Ornstein.docx (262 KB): Professional document format; content should be reviewed for technical depth.
Professional checks
- The profile contains relevant field signals such as pd, lgd, ead, ifrs9.
- The description would be stronger with clearer evidence around ifrs 9, woe, iv, gini, auc, calibration, backtesting, vintage.