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Data Analysis
4 Years Experience
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Machine Learning
1 Year Experience
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Statistics
4 Years Experience
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Reporting
4 Years Experience
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Data Visualisation
4 Years Experience
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Personal Statement

I am a pragmatic and innovative Data Analyst specialising in modelling, visualisation and machine learning. I've completed University with a First Class Honours BSc in Actuarial Mathematics and Statistics and have acquired many technical proficiencies throughout my career. Technical aptitude has allowed me to successfully develop business functions in order to support enhanced operational efficiencies as well as productivity. My excellent communication skills provide me with the foundation to create and maintain strong professional relationships with colleagues and clients alike. I am accustomed to working on numerous work streams simultaneously, and can schedule tasks in accordance with their priority and designated timescales. My adaptive nature nature allows me to easily develop and maintain skills in new working environments. I can collaborate effectively within a team whilst maintaining the self-discipline to deliver independently.

Technical Skills

Languages

85%
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85%

Frameworks & Libraries

99%
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Qualifications

Degree

Subject
BSc, Actuarial Mathematics and Statistics
Grade
First Class Honours Bsc
Year of Graduation
2014

Modules

  • Optimization
  • Time Series
  • Partial Differential Equations
  • Further Inferences and Bayesian Methods
  • Contingencies
  • Linear Algebra
  • CT1-CT5 from Actuarial Institue

Professional Experience

Munich RE

Data Analyst

May 2019 - Present
  • Utilising data between the Actuarial team and the Underwriters in order to monitor and update the reserves requires for the current business needs, this is achieved by creating a unique report for each class of the business where the received premiums and paid claims, outstanding claims and Incurred but not reported claims are presented along with the Actuarial predicted claims and underwriters predicted claims, the Actuarial team and underwriters discuss their predictions compared with factual data and decide on the optimised amount of reserves. The Actuarial were initially stored in an Access Database and underwriter in the SQL Server, to bring both into the same report, data was imported into Excel from both sources and exported back once the ultimates have been settled.
  • Creating and maintaining regulatory report for the Lloyds of London and other regulators of the business, using Python, Excel and SQL server.
  • Migration of the Reserving Databse from MS access into SQL server, the business had decided it would want to re-engineer the current process of Reserving and move from the old and buggy MS access into SQL server. There were hundereds of queries in MS access with each performing a unique taks for the reserving database. and there a handful of macros to refresh the data every quarter in order to have the latest business data. I have created a updated view to replace each query, and in this process we realised not all of the queries were benefitial and we could fit few into one View in SQL server. and for the Macros, I generated unique stored procedures to Select, insert, update and delete data were necessary in order to have the most up to date data, in our tables.
  • Intensive use of Macros, VBA Active x Data object for connection to and from SQL Server and MS Access, to filter data, import filters data into Excel and export the modified version back into the SQL data warehouse.
  • Generating Python scripts to run SQL queries and/or Excel Macros in a single script to automate the process even more.
  • Generating and maintaining KPI reports for the business such as various Claims model, namely various types of claims such as attrition, large and catastrophic claim and the Premiums received in order to visualise the total loss and gains of the business under various categories all done in Power BI.
  • Assistance in Server-side production and allocations of the existing data for the IFRS17
  • Premiums and Claims allocation from class to sub class of the business using existing data, I achieved this using Python Pandas library by creating a matrix of premiums/claims ratio for the existing class, and allocating the classes to sub classes using the ratio matrix.
  • Web scraping data from the net to assist the underwriters and actuaries in their modelling in areas such as War and terrorism categories.
  • Migration of MS access to SQL sever and creating new Views in SQL server to replace the queries in access, and new stored procedures to replace the macros.

Iplato Healthcare

Data Analyst

Sep 2018 to Nov 2018
  • Analysing the output of data from the “myGP” application and producing reports on the results
  • Providing expert business support to develop this new start-up company
  • Recommending means to utilise data analytics in an operational environment and how this could support and develop business intelligence and growth
  • Maintaining the application to ensure it was running at optimal levels
  • Creating data visualisation dashboards and automated campaign reports
  • Developing robust and bespoke data sophistication queries to extract patient and GP practice information
  • Establishing strong lines of communications with external data suppliers
  • Ensuring all process and information flows were running efficiently and effectively
  • Facilitating data extraction techniques using Python, MySQL and SQL Server

National Air Traffic Services

Data Analyst

Dec 2017 to May 2018
  • Conducting intricate analysis of airspace, airport and flight data and radar information to support the efficiencies of Air Traffic Controllers to monitor airspace
  • Creating dashboards for flight delays and danger areas designed to mitigate delays, efficiently use fuel and utilise the available air space
  • Producing forecasts using regression and classification methods in machine learning and ensuring the accuracy of all information provided
  • Developing means to automate existing reports to enhance the reporting functions reliability, accuracy and speed
  • Conducting data analysis and data visualisation
  • Processing statistics
  • Making sure the dashboard and interactive charts created were simplified for the client’s daily use, and could be easily deployed onto the server
  • Creating structed data from unstructured data arrays and online resources
  • Extracting vital data from junk and unstructured data
  • Facilitating technical data presentations in a manner that could be understood by non-technical personnel
  • Identifying and communicating solutions to business issues by justifying machine learning models, and extracting and visualising the contribution of data features
  • Ensuring the data was aligned to the business expectations and suggesting solutions to achieve this
  • Building pipelines to transfer and cleanse data from one platform to another

Achievements

  • Successfully achieved 95% accuracy in Machine Learning Modelling through utilising Python skills and knowledge
  • Created a model that was used to predict 3di-scores, five years into the future, based on two years of historical flight data which achieved 95% accuracy with the root mean squared error 1.05
  • Dramatically reduced manual duties by months through automating reports and tasks
  • Consistently exceeded 85% forecasting accuracy, using regression and classification methods in Machine Learning

BGC Partners

Risk Reporting Analyst

Feb 2017 to Mar 2017
  • Creating daily credit reports which outlined the unsettled trades and escalated the credit team levels to the traders
  • Adjusting trading prices by using the Bloomberg terminal to conduct the final calculations of counterparties exposure
  • Producing risk calculations and risk reporting

TNS

Research Executive

May 2015 to Feb 2017
  • Managing the daily operations of the production department and ensuring the number of samples produced were in accordance with the targets and client specifications
  • Conducting intricate analysis and cleansing of project data
  • Producing suspicious data reports and escalating to the operations centre
  • Translating technical and mathematical data into presentations for non-technical stakeholders to determine the future of projects
  • Creating periodic project reports for clients
  • Identifying and implementing means to enhance efficiencies and productivity through automating reports
  • Creating connections between SQL and Excel
  • Providing the expert function for string, dates, functions, left-right and fuller joins, temp-tables, pivot tables, sub-queries, ADODB, Outlook outward, files and folder manipulation and scripting within T-SQL and VBA
  • Establishing strong client professional relationships