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*Graduate Job* Data Scientist in Fintech (KTP Associate)

Position:                     Data Scientist in Fintech (KTP Associate)

Based at:                     Access Systems (UK) Ltd, Manchester

Salary:                         £28,000 - £34,000(depending on experience)

Closing date:             4th October 2018


The Role:

An exciting opportunity has become available to work full time on an 18 month Knowledge Transfer Partnership (KTP) to embed a machine learning capability within the company to enhance existing cash forecasting solutions and support significant business growth. You will work with data science and machine learning techniques to process, visualise, gain insights, identify patterns and trends, and make predictions on financial, multivariate time series data.


Company Information:

AccessPay (AP) is a cloud-based financial technology business. AP has been recognised by Deloitte as one of the 50 fastest growing tech companies in the UK and the fastest growing Fin-tech company outside of London. AP‘s software allows businesses to automate payment transactions more quickly and securely through one platform. Recent launches include a real-time cash management and analytics tool (BankSense), which is aimed at meeting the needs of global corporations, and a pro-active fraud detection tool, monitoring transactions and payments for anomalous behaviour. Typical clients are corporates with an annual turnover >£100 million.


Qualification Requirements:

An MSc in Computer Science, Artificial intelligence, Statistics, Mathematics, Data Science, or related discipline. Candidates with a good (Hons) degree in a relevant subject such as Data Science or Computer Science with evidence of a conducting a project in data science would also be considered.


Experience and Knowledge Requirements:

It is expected that the candidate has experience with creating fully reproducible and documented end-to-end data science pipelines in a suitable ecosystem (preferably, Python) and familiar with data science techniques such as data extraction, exploration, cleaning, visualisation, as well as model building with inferential statistics and machine learning. Ideally, the candidate has also some experience with techniques for handling and forecasting time series data (e.g., ARIMA) and with deep learning.

Some knowledge of SQL and NoSQL databases is desirable. In addition, the candidate should be comfortable with performing software design, implementation, testing, and version control (e.g., with git and GitHub).

The candidate is expected to have excellent oral and written communication skills with the ability to lead a project from the technical/scientific perspective and to produce research outputs as academic papers publishable at top venues. Some other personal attributes looked for are:




For an informal discussion, please contact Luciano Gerber ( or Dr Keeley Crockett (