Ololade B.

Key Skills

Software

Other

Top Subject Matter

Engineering analytics

policy-guideline optimization

and dataset quality assurance

Top Data Types

Text

Top Task Types

RLHF

Fine-tuning

Data Collection

Freelancer Overview

AI training/evaluation preparation via ground-truth auditing and logical bias checks (RLHF-style feedback readiness). Brings 3+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Other. Education includes Bachelor of Science, Lagos State University (2020). AI-training focus includes data types such as Text, Computer Code, and Programming and labeling workflows including RLHF, Fine-tuning, and Data Collection.

Labeling Experience

Dataset structuring and categorization for training-ready ground-truth data (schema design + validation)

OtherData Collection

Architected structured schemas and categorization logic to systematically organize raw data inputs into ground-truth-ready formats. Implemented input validation and quality assurance controls to eliminate anomalies and preserve 100% data integrity across regional aggregation levels. Focused on tagging, aggregation, and record handling to ensure consistent dataset labeling structure for downstream training. • Designed relational MySQL schemas for structured tagging and aggregation. • Implemented validation rules to prevent malformed or inconsistent records. • Standardized how raw inputs map to categorized outputs for training use. • Maintained secure record-handling protocols to protect data quality.

2025 - Present

Training data preparation: EDA, feature engineering, and validation for supervised fine-tuning readiness

OtherFine-tuning

Conducted dataset preparation workflows that emulate supervised fine-tuning input creation by cleaning, filtering, and structuring raw tabular records. Performed feature engineering to generate high-fidelity categorical variables used as training-ready model inputs. Implemented validation and quality assurance steps to maintain data integrity before model training. • Executed EDA on a 100k+ record dataset to clean and structure inputs. • Created new categorical variables for improved model learning signals. • Applied strict input validation and secure record-handling protocols. • Organized labeled/derived datasets to support downstream supervised training.

2025 - Present

AI training/evaluation preparation via ground-truth auditing and logical bias checks (RLHF-style feedback readiness)

OtherTextRLHF

Performed ground-truth auditing and guideline-precision evaluation to support high-quality AI training and RLHF-style feedback preparation. Focused on identifying inconsistencies, anomalies, and policy-relevant patterns within structured datasets for reliable supervision. Applied logical analysis to challenge biases between conflicting variables (e.g., demographics versus cost structures) to optimize training and evaluation criteria. • Evaluated hidden patterns, anomalies, and structural waste to improve dataset fidelity. • Ensured outputs align with strict technical guidelines and validation expectations. • Reviewed dataset relationships to detect bias and inform refinement of labeling targets. • Prepared data integrity checks to support downstream model training workflows.

2025 - Present

Education

Work History