Subhan Nasir

• Collected, cleaned, and validated operational data from multiple warehouses and branches to ensure reporting
accuracy.
• Developed interactive dashboards in Excel & Power BI to track inventory levels, dispatch plans, and receiving
performance.
• Improved inventory visibility by standardizing reports and implementing search-based tracking for items and
suppliers.
• Conducted branch-wise and category-wise analysis to support demand forecasting and inventory
optimization.
• Designed structured datasets and pivot tables to transform raw operational data into actionable insights.
• Delivered data-driven insights to management, improving planning decisions and reducing stock-out risks.
• Worked with cross-functional teams to improve data quality, optimize data pipelines, and ensure consistent, reliable
reporting.
• Streamlined reporting processes by standardizing data formats and reducing manual analysis effort.

• Developed and tested predictive models using Python and R to enhance forecast accuracy.
• Collaborated with senior data scientists to fine-tune algorithms and evaluate model performance.
• Applied EDA techniques to identify trends and patterns in datasets.
• Participated in discussions to improve data pipelines and model integration.
• Assisted in building automated data pipelines to preprocess large datasets for model training.
• Participated in model evaluation sessions, performing cross-validation and performance tuning.

Developed and deployed predictive machine learning models using Python (Scikit-learn,
TensorFlow) and R to improve forecasting accuracy across business processes.
Collaborated closely with senior data scientists and engineers to refine algorithms, enhance
model accuracy, and implement performance evaluation metrics such as RMSE, MAE, and
AUC.
Applied advanced Exploratory Data Analysis (EDA) techniques to extract actionable insights
and visualize data trends using Pandas, NumPy, and Seaborn.
Participated in strategic discussions to improve the scalability and efficiency of data
pipelines and model deployment processes.
Conducted statistical hypothesis testing, regression analysis, and variance analysis to
evaluate the effectiveness of different operational strategies.
Assisted in the design and automation of ETL pipelines using Python scripts and SQL to
preprocess large-scale datasets for model training.
Participated in model validation and tuning using cross-validation, grid search, and
performance optimization techniques.
Documented modeling approaches, insights, and technical processes to ensure knowledge
transfer and reproducibility.

• Designed and delivered Power BI dashboards to track business KPIs, operational performance, and departmental
progress across multiple domains.
• Translated business requirements into data models and reports, enabling management to make timely, data-driven
decisions.
• Conducted deep-dive data analysis using SQL and R to uncover trends, performance drivers, and improvement
opportunities.
• Collaborated with stakeholders to define key metrics, visualize results, and automate weekly/monthly reporting
processes.
• Built interactive visualizations that improved visibility into user engagement, training efficiency, and project progress.
• Worked with cross-functional teams to improve data quality, optimize data pipelines, and ensure consistent, reliable
reporting.
• Led ad-hoc analysis to answer strategic business questions, enhancing responsiveness and agility in decision-making.

• Utilized SQL and Power Query to extract, clean, and transform large datasets from multiple sources, ensuring accurate
and efficient reporting.
• Developed and maintained Power BI and Excel dashboards to monitor key performance indicators (KPIs), operational
metrics, and business goals across departments.
• Created Excel-based analytics tools for department heads to track trends, performance summaries, and progress
toward strategic targets.
• Conducted trend analysis, variance analysis, and visual storytelling to support business strategy and performance
reviews.
• Partnered with cross-functional teams to gather reporting requirements and delivered insights tailored to various
stakeholder needs
• Automated weekly and monthly business reports, reducing manual reporting efforts by over 60% and increasing data
accessibility for leadership.
• Collaborated with BI and IT teams to improve data accuracy and consistency, leading to more reliable insights and
decisions.

• Assisted in collecting, cleaning, and preparing data for reporting and analysis, ensuring data integrity and consistency
across departments.
• Performed exploratory data analysis (EDA) to identify patterns in student engagement, success rates, and dropout
behavior.
• Created Power BI and Excel dashboards to visualize training outcomes, candidate performance, and placement trends.
• Supported senior analysts in generating weekly and monthly performance reports for management and external
partners.
• Presented insights during internal review sessions, contributing to decision-making around curriculum design and
operational planning.

Developed and deployed predictive machine learning models using Python (Scikit-learn,
TensorFlow) and R to improve forecasting accuracy across business processes.
Collaborated closely with senior data scientists and engineers to refine algorithms, enhance
model accuracy, and implement performance evaluation metrics such as RMSE, MAE, and
AUC.
Applied advanced Exploratory Data Analysis (EDA) techniques to extract actionable insights
and visualize data trends using Pandas, NumPy, and Seaborn.
Participated in strategic discussions to improve the scalability and efficiency of data
pipelines and model deployment processes.
Conducted statistical hypothesis testing, regression analysis, and variance analysis to
evaluate the effectiveness of different operational strategies.
Assisted in the design and automation of ETL pipelines using Python scripts and SQL to
preprocess large-scale datasets for model training.
Participated in model validation and tuning using cross-validation, grid search, and
performance optimization techniques.
Documented modeling approaches, insights, and technical processes to ensure knowledge
transfer and reproducibility.