Classic KTP Vacancy advertisement details

Reference: 3715/3

Location: Manchester, Greater Manchester, M1 4BT
Salary: £28,000 - £34,000 (depending on experience)
Qualification required: MSc in Computer Science
Closing Date: 25 March 2019
KTP Advert Ref: 3715/3
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.


£3,000 to spend on personal training over the course of the project;
attendance at two residential managerial workshops (each of one week’s duration);
opportunity to register on a higher degree (at a reduced or no cost);
opportunity of a permanent position with the company; 70% of host companies make a permanent job offer to their Associate at the end of the project.

To apply: Please attach your CV and a covering letter, detailing how you meet the person specification, to the online portal.

For an informal discussion, please contact Luciano Gerber ( or Dr Keeley Crockett (
Skills required: Qualification Requirements:

An MSc in Computer Science, Artificial intelligence, Statistics, Mathematics, Data Science, Economics, 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:

Capable of working in a team environment.
Capable of working independently, taking direction, making decisions and managing workload.
Curious and inquisitive by nature and excelling at storytelling through analysis of data.
Enthusiastic and self-motivated.
Capable of communicating complex concepts in a clear manner to a wide-ranging audience.
Knowledge Base Partner: Manchester Metropolitan University
Company partner: Access Systems (UK) Limited
Company business area: Provide cloud-based financial technology offering a range of payment and cash management products (direct debit, SEPA, faster payments, SWIFT, multi-bank cash management).
Contact: Susan Connor, Ormond Building
Lower Ormond Street
M15 6BX
, Tel: 01612472821
Additional Info: None

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