Candidate Fact Confirmation Checklist
Verified operational evidence record: 44 confirmed facts (K01–K44), employer integrity corrections, and 1 open item.
44 Confirmed Facts
1 Optional Open Item
Employer Integrity & Scope Governance
- Story S10 Employer Integrity: Verified at CoreLogic (Resume Fact F10: RevOps MDM automation). Excised Internet Brands and '3 business divisions' from S10. Uses '~150 orphan parent-child accounts'. Cross-BU budget alignment (F12) moved to pitch bridge.
- Story S11 Engagement Context: Verified at Deloitte Consulting as a commercial forecasting client engagement. Highlights transparent co-design after client director pushback.
- Tag Taxonomy: All old placeholder tags retired. Exactly 1
[OPEN: Number of leases or renegotiation packages prioritized, optional]tag exists in Story S01 result field only.
| ID | Story | Company | Beat | Verified Fact Statement | Status |
|---|---|---|---|---|---|
| K01 | S01 | Deloitte | ROLE | I personally wrote the Python extraction scripts and SQL analytical queries; the engagement manager led the client real estate director reviews. | Verified |
| K02 | S01 | Deloitte | CONFLICT | Client asset managers initially resisted portfolio-wide scrutiny, saying lease covenants had too many localized landlord exceptions to model systematically. | Verified |
| K03 | S01 | Deloitte | NUMBER | [OPEN: Number of leases or renegotiation packages prioritized, optional] — Resume anchor verifies $9M+ across 500+ locations. | Optional Open |
| K04 | S01 | Deloitte | OUTCOME | The client real estate committee approved the findings to start formal landlord renegotiation. | Verified |
| K05 | S02 | CoreLogic | ROLE | I was the sole data modeler and business translator: I defined the metric logic and led consensus workshops across all 8 merchandising teams. | Verified |
| K06 | S02 | CoreLogic | CONFLICT | Category leads initially defended their own legacy definitions of a store visit, because each had been reporting with a different definition. | Verified |
| K07 | S02 | CoreLogic | NUMBER | Replaced 3 conflicting departmental definitions with 1 production metric definition. | Verified |
| K08 | S02 | CoreLogic | OUTCOME | The metric was integrated into the core enterprise data warehouse as the standard store-traffic dimension. | Verified |
| K09 | S03 | CoreLogic | ROLE | I formulated the CTG decomposition formula and built the automated reporting workflow in Python and SQL. | Verified |
| K10 | S03 | CoreLogic | CONFLICT | Merchandising leads resisted at first because simple percentage changes were easier to understand than additive contribution math. | Verified |
| K11 | S03 | CoreLogic | NUMBER | Eliminated approximately 12 hours of weekly manual reporting across category analytics teams. | Verified |
| K12 | S03 | CoreLogic | OUTCOME | Business reviews adopted CTG as the way to evaluate category performance across 500+ stores. | Verified |
| K13 | S04 | CoreLogic | ROLE | I was product owner and developer: I gathered store layout requirements and partnered with store operations leads to capture operational constraints, and I built the interactive tool, including all of the backend scenario calculations. | Verified |
| K14 | S04 | CoreLogic | CONFLICT | Store operations leads were skeptical that a centralized tool could account for regional differences in store layouts. | Verified |
| K15 | S04 | CoreLogic | NUMBER | Enabled store operations teams to simulate remodeling scenarios across 500+ locations in under 10 minutes. | Verified |
| K16 | S04 | CoreLogic | OUTCOME | Executive leadership approved the annual capital expenditure plan based on the tool's scenario outputs. | Verified |
| K17 | S05 | CoreLogic | ROLE | I worked with client platform engineering to define the DAG architecture and personally migrated the data transformation tasks. | Verified |
| K18 | S05 | CoreLogic | CONFLICT | Two engineering teams were reluctant to migrate their scheduled cron jobs because of concerns about pipeline downtime during release. | Verified |
| K19 | S05 | CoreLogic | NUMBER | Consolidated pipelines from 4 separate repositories into 1 centralized orchestration environment. | Verified |
| K20 | S05 | CoreLogic | OUTCOME | Pipeline failure resolution times dropped noticeably, and engineering teams gained end-to-end visibility. | Verified |
| K21 | S06 | CoreLogic | ROLE | I wrote the JavaScript and HTML integration code for the Tableau Extension API and consulted with dashboard designers on the UI flow. | Verified |
| K22 | S06 | CoreLogic | CONFLICT | The client BI team initially opposed the extension because of the development time relative to the value it would return. | Verified |
| K23 | S06 | CoreLogic | NUMBER | Replaced 4 disconnected reporting dashboards with 1 unified interactive view. | Verified |
| K24 | S06 | CoreLogic | OUTCOME | The client analytics team incorporated the extension into their standard executive reporting template. | Verified |
| K25 | S07 | Deloitte | ROLE | I designed and implemented the automated RAGAS evaluation pipeline across 4 retrieval and generation metrics. | Verified |
| K26 | S07 | Deloitte | CONFLICT | Engineering felt manual spot-checks were faster, while product leads felt synthetic evaluation was too artificial to reflect real user prompts. | Verified |
| K27 | S07 | Deloitte | NUMBER | Built an evaluation benchmark of 150 prompt-response pairs to evaluate retrieval context and generation accuracy. | Verified |
| K28 | S07 | Deloitte | OUTCOME | RAGAS was adopted as the standard quality gate before deploying pipeline prompt updates. | Verified |
| K29 | S08 | Deloitte | ROLE | I configured the LLM Guard scanners, defined risk thresholds, and integrated the scanning module into the response generation pipeline. | Verified |
| K30 | S08 | Deloitte | CONFLICT | Product stakeholders worried that aggressive safety scanning would add noticeable latency to user queries. | Verified |
| K31 | S08 | Deloitte | NUMBER | Kept scanning latency overhead under 85 milliseconds per request. | Verified |
| K32 | S08 | Deloitte | OUTCOME | The guardrails intercepted compliance risks and toxic completions without degrading user experience. | Verified |
| K33 | S09 | CoreLogic | ROLE | I built the SQL funnel attribution models and designed the pipeline conversion dashboards in revenue operations. | Verified |
| K34 | S09 | CoreLogic | CONFLICT | Sales reps felt conversion reporting unfairly blamed their closing skills rather than poor inbound lead quality. | Verified |
| K35 | S09 | CoreLogic | NUMBER | Pinpointed a 22% drop-off between sales qualification and the initial solution demonstration. | Verified |
| K36 | S09 | CoreLogic | OUTCOME | RevOps leadership restructured the demo handoff workflow to improve qualification progression. | Verified |
| K37 | S10 | CoreLogic | ROLE | I designed and wrote the automated Python reconciliation script and set data validation rules for parent-child accounts. | Verified |
| K38 | S10 | CoreLogic | CONFLICT | The operations team was used to manual spreadsheet adjustments and resisted trusting an automated hierarchy script. | Verified |
| K39 | S10 | CoreLogic | NUMBER | Reconciled ~150 orphan parent-child accounts. | Verified |
| K40 | S10 | CoreLogic | OUTCOME | The automated process saved ~8 hours per week and provided clean hierarchy data for monthly reporting. | Verified |
| K41 | S11 | Deloitte | ROLE | I built a high-dimensional econometric forecasting model for a client forecasting engagement. | Verified |
| K42 | S11 | Deloitte | CONFLICT | The client director rejected the model as a black box because the business logic was opaque to non-technical stakeholders. | Verified |
| K43 | S11 | Deloitte | NUMBER | Spent 3 weeks co-designing a transparent, driver-based forecasting model with the client team. | Verified |
| K44 | S11 | Deloitte | OUTCOME | The co-designed model was adopted for strategic planning, teaching me to prioritize interpretability and stakeholder co-ownership. | Verified |