Dr. Wasif Masood

Automated Scientific Discovery – Use of ML methods to ease the cycle of scientific discovery. Implemented an automated ML platform with several explainable AI and causality frameworks.

Development of a feature-store to help boost model development process. Automation of machine learning models using the complete MLOps stack of AWS including CI/CD, model pipelining, deployment, and scheduling.
ETL scripts to load data google analytics, flatten the key/value structure of the data, and finally save it in AWS S3. Built a model for text labeling to better understand student feedbacks.

Implemented a search engine for 3D printable models. The platform is live https://3dfindable.com.
Python | Django| Selenium | VGG16

Implemented a search engine for DIY projects. The platform is live at https://findingdiy.com.

Python| Django | Selenium | Hugging Face T5 | NLTK | Sentiment Analysis

MODELS FOR CROSS-SELLING 02.2021 - 11.2021
Several machine learning models are built to help facilitate the cross-selling activities of a telecom operator. The models are built to, 1) identify the likely customer for cross-selling, 2) the most suitable product for that customer and lastly, 3) the right
channel to approach that customer. Campaign success rate increased by 17%.
Python | SQL | DWH | CI/CD | Oozi | Tableau | Google Analytics | Big Query | Boosting | Bagging | Optuna

MODELS FOR UP-SELLING 03.2021 - 08.2021
Built machine learning models to help facilitate the up-selling activities. This is to facilitate the base-management challenges, i.e., increase ARPU per customer by up-scaling their tariffs. Campaign success rate increased by 28%.
Python | SQL | DWH | CI/CD | Oozi | Tableau | Google Analytics | Big Query | Scikit-Learn | Optuna


LIFT AND SHIFT 08.2020 - 01.2021
The end-to-end execution of household identification use-case was too time consuming and was block other pipelines. Therefore, a lift and shift approach was adopted to fully migrate the use-case to the GCP, including its ETL.
Pyspark | Cloud Storage | GCP Dataproc | GCP Data Studio | GCP Cloud Composer | AirFlow

HOUSEHOLD IDENTIFICATION 04.2020 - 01.2021
Built a model to identify customers living in same household for a telecom operator. Extensive explorative data analysis is performed to identify potential levers to help improve the matching criteria.
Pyspark | Hive | SQL | CI/CD | Oozi | Tableau | Google Analytics | Big Query | MLIB | Graph Theory

PROACTIVE MAINTENANCE 07.2019 - 03.2020
Proactively detect the root causes of network problems. A thorough time-series analysis is performed and both induction (ML) as well as deduction-based models are built. Time to resolve the ticket was reduced by half.
Pyspark | Hive| SQL | Power BI | CI/CD | Oozi | Tableau | ElasticSearch

TARIFF OPTIMIZATION 02.2019 - 05.2019
Sketch a relation between the old products and current customer base and identify any gaps in the current portfolio which hinders the operators in increasing their customer base. Additionally, analyze the need for any shadow products for
retention and customer base management.
R | R FOR OPERATIONS RESEARCH | Google ortools | Crone | R&D| Clustering

VALUE BASED NETWORK ROLL-OUT 05.2018 - 02.2019
Based on several customer satisfaction KPIs, a model is built to efficiently target network rollout and upgrade activities by investing in sites with poor network quality scores. This results in saving of several million euros as network rollout is a high
budget activity i.e., a small % improvement in investment strategy results in considerable savings.
Pyspark | Hive | Power BI | Traffic Forecasting | xgboost | Oozi | R&D | Analytical Models

CUSTOMER EXPERIENCE 09.2017 - 05.2018
Several models are built to measure customer experience on various service aspects. E.g., customer satisfaction via NPS, bill shock, customer experience w.r.t network quality, as well as service line interactions. Several inductions (ML) and deduction
based models are built.
Pyspark | Hive | Python | SQL | Power BI | Oozi | Analytical Models

CHURN & RETENTION 05.2017 - 09.2017
Built a model to predict customer churn. Additionally, a detailed analysis on churn reasons is performed by calculating the SHAPLEY values. After A/B testing, an ultimate 17% reduction in churn was achieved.
Python | SQL| Power BI | DNN | Boosting | SVM | Bayesian Statistics | SHAPLEY | Crone Job | Scikit-Learn | Keras

TRAFFIC LOAD FORECASTING 04.2018 - 07.2020
Forecasting network traffic per cell basis. This project was the pre-requisite of value-based roll-out.
Python | SQL| LSTM| RNN | Boosting | SVM | GNU | Scikit-Learn | Keras | FBProphet | Optuna | State-Space Modeling | ARIMA | Kalman Filter | DeepAR | AWS SageMaker