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Data Scientist

Masood Syed, ROCHDALE, United Kingdom


1 - 4 years

Other titles

Data Analyst, Machine Learning


Data Science, Data Analysis, Machine learning, DATA ANALYSE OG VISUALISERING, Statistiker , Forecasting, Excel, NLP, Python, Microsoft azure, Power BI   + 6 more

I'm offering

I believe that my educational background, experience acquired via working through different industries and skillset makes me a good potential candidate to take up tasks at your organisation. A description of what I can bring on the table is given below:

I completed my Bachelor’s in computer sciences followed up by a Master’s in Data Science from Lancaster University. I have had to fight great odds to continue my education with a handicapped father – who went through two brain strokes with being a heart-patient and diabetic at the same time. My undergraduate degree provided me with a solid foundation on programming and databases whereas: my master’s degree allowed me a chance to upskill myself in statistics, machine learning, programming for data science, time-series analysis and research methodologies.

Being an active sportsman, I have learned to lose, do gap-analysis, train, improve and come back to achieve success. For the years 2018/19, I am representing Lancashire County Badminton second team and currently holds a national ranking of 120th. This sport has taught me discipline and given me sheer will to not giving up regardless of hurdles.

I have had opportunities to work for companies like Vodafone, British Gas, Barclays Bank Plc, AstraZeneca and Transport Systems Catapult along with teaching experience from University of Salford. The below examples will demonstrate my ability for stakeholder management, communication skills, ability to come up with solutions to loosely defined business problems using different forms of research and analytics using vast amounts of data.

Barclays Bank Plc

At Barclays Bank Plc, Credit Management Unit, I performed a quantitative research study into the deletion of non-functional contact numbers more efficiently from 3 available systems.

Problem Statement:

The policy required for employees on the front-end to go into 3 different customer management systems to delete one single non-functional contact number resulting in extra time taken to wrap up calls.

Research & Analysis:

Firstly, I conducted a survey from employees to ascertain their level of satisfaction with ongoing structure and an idea of average time it takes for them to complete the task based on their skills. The survey provided me with an opportunity to highlight sentiment and problem with figures to the higher management. The survey results helped in making the management aware of this problem and got me an opening to carry further investigation.

Secondly, I collaborated with different departments including operations, dialler management, business analysts, external partners for macro-creation to identify the right sources of data for conducting correct analysis. Several sources of data were consulted before the identification of relevant information and nearly one-million observations were extracted into Excel to highlight summary statistics on several parameters including talk-time, wrap-up time, call-waiting time. A multivariate linear regression was used to quantify and prove a relationship between several linked parameters. This information was used to highlight the snowball effect of an inefficient mechanism in place on finances and internal employee satisfaction.

Finally, two solutions were presented with a comparison of financial gains for the company and job satisfaction for internal employees. A potential saving of approx. two hundred and fifty thousand pounds was forecasted for the organization if the solution was to put in place. This research was also selected to be presented in front of the then CEO of Barclays and as a result a team was dedicated to validating the findings of my research prior to acting upon it.

Lancaster University – Master’s in Data Science

During my time at Lancaster University, I worked with a few external companies on several interesting problems involving real-world data. A set pattern was followed to gain an understanding on:
• Nature of business/products/services
• Clients/End Users
• Problem Statement
• Data/Information available
• Format/shape/structure of data
• Setting milestones to gauge progress
• Presentation of results

For all these projects a constant stream of communication was kept open with stakeholders to ensure on time delivery of solutions, quality and the desired output.

A couple projects completed are as follows:

Project 1:

Company: Anonymous Financial Institute

Problem: Optimise the number of customers accepted for financial products with focus on unsecured loans, while reducing the loss using statistical and machine learning techniques
Methods: Logistic Regression and Decision Trees were implemented, and efficiency measured via area-under-the-curve (AUC).

Result: A decision tree model was recommended based on interpretability of diagnosed rules and higher accuracy then logistic regression.

Project 2:

Company: Environmental Agency, UK

Define patterns of chemicals in UK water sources to help regulate the industry

Data: Data was collected for different chemicals present in water sources all over the UK. The size of the data was in millions of observations

Methods: Clustering, Association Rule Mining & Neural Networks

Result: Clustering showed how chemicals were grouped at different kinds of water sources. Association rule mining shed light on the linkages of chemicals and neural networks were used to identify the temperature and pH value of water based on several chemicals.

AstraZeneca – Data Science Internship

Completed a project to predict survival rates of breast-cancer patients using clinical trials data from ProjectDataSphere platform. The study used an ensemble learning technique called “Gradient Boosting” on untreated, pre-treatment observations to carve out factors affecting the survivals of patients. A thorough understanding of clinical trials was obtained through coordinating with the help of a clinical trial expert from AstraZeneca. The survival rate accuracy achieved was over 70% implying that a rigorous data pre-processing could enhance the results. Once again, there was an element of stakeholder management, regular communication with clinicians, cancer researcher, statistician and programmers at AstraZeneca.

Further professional experience is highlighted in the CV.

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My education

2017 - ?
University of Sheffield
PhD in Data Analytics, Data Science
Data Fusion for Smart Cities

2014 - 2016
Lancaster University
Master's, Data Science
Master's in Data Science

Statistical Modelling
Statistical Inference
Data Mining
Data Mining for Sales, Marketing & Finance
Applied Data Mining
Programming for Data Scientists
Fundamentals of Data Science

My resume

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