ECOVIS Saudi Arabia (ECOVIS AL SABTI)

Senior Consultant I - Data Management & Data Privacy

The Coca-Cola Company

The Coca-Cola Company ~ Coca-Cola Eurasia Middle East

 Translated business requirements into technical language by means of complex Structured Query Language (SQL).
- Free Liquid Loyalty Campaign: Conducted a root cause analysis to address the decline in participation, leading to the recovery of 70% of historical customers.
- Analyzed trends and patterns based on large data sets from multiple data sources (internal databases, market data, and thirdparty sources) to extract meaningful insights for Daha Daha Loyalty App at Coca-Cola Turkey.
- Developed clear and concise analytical reports such as executive summary reports on weekly and monthly basis as well as catered adhoc requests to drive growth.
- Developed and communicated key findings and recommendations to relevant stakeholders by means of data storytelling for  action based on data-driven insights.
- Worked closely with a team of data engineers to check the data quality and identify irregularities, inconsistencies and anomalies in data in order to notify the relevant data owner to ensure data accuracy, integrity and completeness before ingestion.
- Identified areas for improvement, and recommended changes to increase consumer engagement on Daha Daha App as well as enhance its operational efficiency.
- Maintained the Harmonization Code for Daha Daha Loyalty App. Achieved more than 80% accuracy for SKU Name of brands listed under The Coca-Cola Company.
- Conducted day to day reporting and collaborated with cross-functional and cross-regional teams in sprint plannings, daily scrum sprints, retrospective sessions and business meetings at Daha Daha Intelligence Center (DDIC).
- Tools: Microsoft Azure Synapse Environment, SQL, Appsflyer, Python (Pandas, Numpy), Anaconda Jupyter Notebook, Visual Studio Code, Microsoft PowerBI

Data Analyst

The Disrupt Labs

Dashboard for Dwell Time:
- To create live stream dashboard for monitoring the dwell time on counters and shelves.
- Connecting dashboard with database in real time and troubleshooting connection issues.
- Securing the dashboard by means of password protection to ensure data confidentiality and to maintain dashboard privacy.
- Interacting with associated stakeholders regularly and providing daily reports.
- Exploring Microsoft PowerApps to develop application for dwell time monitoring.

Non-Invasive Transabdominal Fetal Pulse Oximetry and Its Effect in Reduction of Caesarean Section Deliveries.
- To build, train and fit a machine learning model to predict the mode of delivery (Caesarean Section or Spontaneous Vaginal Delivery) during childbirth based on various pre-birth and post-birth parameters taken from the expectant mothers and her baby.
- Assessing numerous machine learning models related to supervised learning to get an insight into each model to identify the most appropriate model i.e., Logistic Regression for the given problem.
- Performing Exploratory Data Analysis to identify patterns, extract insights, test hypotheses, check assumptions and discover anomalies in the given data set.
- Data preprocessing which encompasses imputing missing values, handling corrupted data and splitting the imported data set into training and test data sets.
- Performing Feature Selection on the data set by means of various statistical tests such as Fisher’s Exact Test for categorical variables and Student’s T-Test for continuous numeric variables.
- Model validation and model evaluation of the logistic regression model based on different performance indicators and techniques.
- Pitching the project idea to gynecologists and obstetricians to obtain the requisite raw data for building the desired predictive model.
- Collecting patients’ data from maternity hospitals and communicating with non-engineering and engineering stakeholders.

Machine Learning Engineer

Institute of Emerging Careers

Data Cohort, Batch 4

-SQL
-Relation Database Management System (RDBMS) (MySQL Workbench)
-Data Analytics (Google Data Analytics Professional Certificate)
-Data Wrangling & Data Cleansing (Pandas)
-Exploratory Data Analysis (EDA)
-Pandas and Numpy
-Data Visualization (Matplotlib, Seaborn & Plotly)
-Spreadsheets (MS Excel, Google Sheets)
-Python
-R
-Dashboards (Power BI, Tableau)
-Cloud Data Warehouse (Google Cloud Platform (GCP), Google BigQuery)

The cohort assessment includes three major capstone projects to help trainees in gaining exposure to real life business scenarios.

Data Cohort Trainee

ibex

Worked for the Walmart US campaign as a Customer Experience Champion.

Responsibilities included:
-Providing customers with prompt and appropriate resolution by as per Walmart US policies via email, chats and calls.
-Communicating between customers, third party marketplace sellers, customer resolutions team (CRT) and store team.
-Keeping up to date with revised Walmart policies as well as known issues and heads-up by clients.

Customer Experience Champion (Walmart US)

An optimist and an adventurer who has embraced a professional career detour from electrical engineering, seeking to venture into the realm of data science, machine learning, and AI. My enthusiasm lies in working with data to drive action and solve real-life problems.

Currently, I am working full-time as a Machine Learning Engineer at The Disrupt Labs on a research and development-based medical project called Non-Invasive Transabdominal Fetal Pulse Oximetry and Its Effect on Reduction of Caesarian Section Deliveries. 

My core responsibility in this role is to study numerous machine learning models followed by fitting the most appropriate model on a dataset acquired from expecting mothers. The motive behind this is to predict via data-driven decision-making, whether or not a Caesarian Section procedure should be performed on an expecting mother to deliver her baby.

In addition to that, I am also involved with the team responsible for pitching the project in front of gynecologists to persuade them into providing requisite raw data for building the desired machine learning model. Also, I regularly attend frequent meetings and participate in day-to-day collaborations with the team of biomedical engineers, medical staff, and other doctors and professionals associated with the project. Hence, this role has inculcated in me a variety of soft skills needed to be a team player and the desired employee.

Currently, I seek to land a new, challenging, and growth-oriented role in a machine learning and data science environment, where I can contribute significantly to improving business through data-driven decisions, solve problems as well as work in any capacity that I am required to.

I adamantly believe that leveraging technological disruption to revolutionize the world and make it a better place is the way of the future.

Technical Skills:
-Programming Languages: Python, C++
-Machine Learning: Regression (Simple Linear Regression, Multiple Linear Regression, Decision Tree Regression, Random Forest Regression), Classification (Logistic Regression), Deep Learning (Simple Neural Network),
-Data Science: Data Preprocessing (Scrubbing/Cleansing, Feature Scaling, Encoding Categorical Data, Splitting data into training and test sets).
-Libraries: Python (Sci-kit Learn, Pandas, Numpy, Matplotlib, Keras)
-Tools: Jupyter Notebook, GitHub.

I can be reached via email: osama.hassan.hhs@gmail.com

Looking forward to getting in touch and making meaningful connections.

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