Sara Sousi

🎓 Education

MRes Bioinformatics in Oncology (Health Data Science) @ Imperial College London (2019-2020)
BSc (Hons) Mathematics @ The University of Warwick (2016-2019)
Short Course in Computational Biology @ UCL (Summer 2018)
A-levels: Mathematics (A*), Further Mathematics (A*), Physics (A)
AS-levels: Chemistry (A), Greek (A)

Senior Data Science Consultant @ EY - Strategy & Transaction (April 2023 - Present)

Led the opportunity sizing for the NHS One Digital Citizen and Shared Care Records pathway used to feed into the business cases to allocate £3.5M for transforming social care with technology.
Independently reviewed the sales forecast model of a $7B pharmaceutical portfolio’s sales using ETS in R, improving accuracy and authoring a detailed report.
Developed a multi-criteria decision analysis framework and opportunity sizing model for NHS Digital Channels for the NHS App opportunity sizing, integrating various weighted factors.
Engineered a SQL-based data processing pipeline for Royal Papworth Hospital, automating patient identification and saving 10+ hours weekly; conducted a Qlik Sense workshop within 48 hours.
Independent algorithm reviews of start-ups and biotechnology companies and conducted the commercial opportunity of their application and use in order to generate revenue.

Honorary Research Fellow @ UCL - Department of Surgical Biotechnology (January 2023 - Present)

I lead all the data analyses for all studies, notable example was utilising NLP for sentiment analysis on post-botulinum toxin injection survey; our publications have reached the news multiple times regarding the regulation and safety of non-surgical aesthetics (Botox, fillers) in the UK.
Contributed to the first MedEd platform in the metaverse for non-surgical aesthetic education.
Created StepUp, a collaborative research platform for medical students and junior doctors, winning two prizes and £250 in AWS credits at the Royal Free x PLASTA Hackathon 2022.
Supervising multiple medical students in our research group across different research projects.
Supervised by Professor Afshin Mosahebi

Data Science Consultant @ EY (September 2021 - March 2023)

Independently validated a £206 billion Markov chain model using advanced statistical techniques in R.
Created a dynamic web scraping and automated reporting tool for NHS England, reducing report generation time to under 10 minutes, used for bids and client conversations; requested to be bought.
Implemented a Python-based ETL pipeline for Qualtrics and proforma data, enabling real-time updates and reducing manual handling, reducing manual data handing by 80%.
Worked with a health information exchange (HIE) in the MENA region and the commercial data opportunity of the population’s healthcare data environment; initiated a new revenue stream by linking population health metrics with dental data.

Honorary Research Fellow @ KCL - King’s Health Partner’s (June 2021 - Present)

Principle bioinformatician of the CEDiD study - prediction of COVID-19 infection using data form wearable smartwatches prior to symptom on-set
Using bash to pre-process the data and R for the analysis of 30 participants wearable data over 30 days, including heart rate, basal temperature, blood volume pulse, and accelerometer data sampled at 32Hz each.
Supervised by Professor Anne Greenough

App Developer @ Virtuoso (June 2021 - December 2021)

Worked on the prototype of the mobile application of Virtuoso, a MedEd / EdTech start up using Flutter, for deployment in iOS and Android mobile phones. Exited, as the founding team wanted to take another direction.
Virtuoso entered demo day, an annual event where London’s leading universities (Imperial, UCL and KCL) come together to showcase the best and brightest start-ups. Virtuoso is powered by King’s Accelerator.

Researcher @ Imperial College London - Institute of Reproductive Developmental Biology (Sept 2020 - Sept 2021)

Developed a machine learning model using LASSO regression to predict the progression of high-grade serous ovarian cancer patients, leveraging RNA-Seq gene expression data and MYLK3 gene beta methylation values. Conducted model development and analyses in R.
Supervised by Professor Sadaf Ghaem-Maghami

Postgraduate Researcher @ Imperial College London - Institute of Reproductive Developmental Biology (Sept 2019 - Sept 2020)

Investigated the effect of alcohol consumption, BMI, and smoking on the prognosis of serous ovarian cancer patients; further build a logistic regression machine learning model predicting the exposure levels using methylation (Illumina 450k) and gene expression data in R.
Supervised by Dr James Flanagan

Python, R, SQL, Bash, Flutter, Power BI / DAX, R Shiny

English (native), Greek (native), French (GCSEs), Spanish (A2), Russian (A2)

For a full list of my Publications please refer to here.