- Collaborated with clients to enhance operational efficiency and reduce shipping delays. Leveraged advanced analytical techniques and technologies to provide impactful recommendations, resulting in significant operational cost reduction.- Specialized in developing and implementing cutting-edge algorithms, machine learning models, and Natural Language Processing systems. Achieved notable improvements in shipment tracking accuracy, ensuring streamlined logistics.- Managed large volumes of structured and unstructured data, ensuring scalable and high-performance data solutions for faster processing, contributing to enhanced operational efficiency.- Quantitative analysis of data issues revealed valuable patterns and optimization opportunities, leading to a reduction in cargo damage incidents. Effective communication of findings through oral presentations and written reports fostered collaboration with Data & Analytics teams and clients, contributing to increased client satisfaction.

I teach wide variety of courses, like Programming, Mathematics, Probability, Operation Research, Machine Learning, Stochastic Processes etc

I teach wide variety of courses, like Programming, Sequential Optimization, Probability, Operation Research etc

- I worked on uplifting sales, customer retention for Auto & General, ENEL and TIM by employing statistical modelling, bayesian statistics, predictive modelling and forecasting, business intelligence and machine learning algorithms. - Designed and implemented models using regression techniques, classification methods and bayesian statistics using STAN through analyzing client’s historical data and optimizing the client’s overall revenue. Implemented a process of creating customer segments for marketing purposes using gradient boost algorithm.

I was part of Research and Development team and I have worked in different areas:- Time series data is regularly generated from production processes and I developed mechanism to identify anomalies in run time to avoid any future hazardous situation.- Production process often enters into phases where can help to predict the future behaviour of processes. To deal with such challenges I worked on timer series pattern detection.- In a production process, proper inventory management and scheduling is required. Ideal scenario is not to dump a lot of raw material on production site or not to observe stock out. To provide a solution for Unilever Pakistan, I used inventory modeling, routing, sequencing algorithms etc.- In production process, often the data generated by sensors are not aligned due to Gross Errors, Random Errors, Systematic Errors. To tackle these challenges, I worked on data reconciliation using Non-Linear Optimization.- When processes fail, identification of Root Cause is first and foremost important step. I developed statistical Root Cause Analysis mechanism to help management identify the critical components.- When process enters into alarm state, it takes some time to fail. This is the time to avoid severe consequences and it is called Remaining Useful Life (RUL). I used Hidden Markov Model, Deep Learning to identify this grace period.