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Dr. Wasif Masood

Full Stack Data Scientist

Works remotely from Vienna

  • 48.2083
  • 16.3725
  • Suggested rate 800 € / day
  • Experience 7+ years
Propose a project The project will begin once you accept Dr. Wasif's quote.

This freelancer is available full-time but hasn't confirmed their availability in over 7 days.

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Location and workplace preferences

Vienna, Austria
Remote only
Primarily works remotely


Project length
  • ≤ 1 week
  • ≤ 1 month
  • Between 1-3 months
  • Between 3-6 months
  • ≥ 6 months
Business sector
  • Architecture & Urban Planning
  • Arts & Crafts
  • Automobile
  • Aviation & Aerospace
  • Banking & Insurance
+45 other
Company size
  • 1 person
  • 2-10 people
  • 11 - 49 people
  • 50 - 249 people
  • 250 - 999 people
+2 other


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Verified email



  • English

    Native or bilingual

  • German



Skills (24)

  • Beginner Intermediate Advanced
  • Beginner Intermediate Advanced
  • NLP
    Beginner Intermediate Advanced
  • Databases
  • Beginner Intermediate Advanced

Dr. Wasif in a few words

I have over 9 years of experience in machine learning and AI. I have worked with both on-premise and cloud based big data solutions. I have built use cases delivering several hundred thousand € profit, purely based on data science and ML based solutions. I have also supported several projects as a data engineering where I have built ETL pipelines between on-premise and cloud based solutions. Since last two years, I have taken the managerial role of leading the big data department which comprises of several data scientists and engineers. There, I have gained ample experience of interacting with C-level executives on progress updates and strategy road maps. I have also led a group of senior researchers from universities on several projects about model bias, privacy, and explainable AI.





June 2022 - Today (9 months)

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.
Dask Ray Pytorch Optuna HyperOpt SHAP LIME Explainable AI

IU International University of Applied Sciences

Education & E-learning

Full Stack Data Scientist  - As a freelancer

Remote (Europa)

March 2022 - Today (1 year)

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.
AWS Sagemaker python dask SQL Airflow Athena redshift AWS FeatureStore



Consultant  - As a freelancer

Erlangen, Germany

August 2021 - October 2021 (2 months)

Implemented a search engine for 3D printable models. The platform is live
Python | Django| Selenium | VGG16



Consultant  - As a freelancer

Erlangen, Germany

April 2020 - June 2020 (2 months)

Implemented a search engine for DIY projects. The platform is live at

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

T-Mobile GmbH


Lead Data Scientist

Vienna, Austria

March 2017 - December 2021 (4 years and 9 months)

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

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

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

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

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

Smasung SDI

Mechanical Engineering

Application Engineer

Graz, Austria

July 2016 - February 2017 (7 months)