Mujeebur Rehman

Developed descriptive, predictive, and prescriptive ML models to support institutional decision-making for an adaptive learning platform — identifying performance gaps, monitoring KPIs across 500+ users, and generating evidence-based recommendations to improve learning outcomes by 40%.Built NLP-based performance monitoring dashboards and statistical analysis pipelines to detect at-risk users; enabled leadership to act on data-driven interventions 3x faster through clearly presented, actionable reports.Conducted model evaluation using Accuracy, F1, Precision, Recall, and RMSE; implemented A/B testing and gap analysis frameworks to track progress against targets — directly mirroring OBEF KPI monitoring and CAP follow-up requirements.

Self-motivated, with a strong sense of personal responsibility.
Excellent communication skills, both verbal and written.
Proven ability to learn quickly and adapt to new situations.
Skilled at working independently and collaboratively in a team environment.
Worked well in a team setting, providing support and guidance.
Passionate about learning and committed to continual improvement.

Analyzed large datasets to identify trends, informing strategic decisions that enhanced operational efficiency and effectiveness.
Collaborated with cross-functional teams to streamline data collection processes, ensuring data integrity and accuracy across reports.
Conducted ad-hoc analysis to support business initiatives, delivering insights that contributed to informed decision-making.
Developed and maintained dashboards using data visualization tools, improving stakeholder access to critical performance metrics.
Improved decision-making processes with accurate data analysis and visualization techniques.
Utilized data visualization tools to effectively communicate business insights.

Designed and deployed 5+ ML models and NLP pipelines processing 100,000+ records; translated complex model outputs into clear management reports and data visualisations for senior, non-technical stakeholders across HR, Finance, and Operations.Built 10+ interactive Power BI dashboards (advanced DAX, Power Query) improving cross-departmental reporting efficiency by 30%; developed time-series forecasting models improving prediction accuracy by 28% to support planning and resource allocation.Conducted statistical analyses to identify operational performance gaps and trends; prepared technical documentation, SOPs, and corrective action frameworks to drive continuous improvement — experience directly transferable to OBEF compliance and academic quality reporting.Automated data collection, cleaning, and preprocessing pipelines using Python (Pandas, NumPy), ensuring data quality and integrity across 100,000+ records for reliable downstream analytics and institutional decision support.

Analyzed and interpreted complex datasets to support decision-making processes, enhancing operational efficiency and data-driven strategies.
Developed interactive dashboards using visualization tools to present insights, streamlining reporting and improving stakeholder communication.
Converted raw datasets into meaningful formats by performing extensive pre-processing activities such as normalization and transformation.
Supported financial analysis by consolidating and examining budget data, aiding in cost optimization efforts.

Developed scalable data-driven web applications with integrated REST APIs; delivered technical training to users and prepared platform documentation — demonstrating communication skills and ability to support diverse, non-technical user bases.