Data Scientist (KTP Associate)

AccessPay

Manchester

28k - £34k

Exciting opportunity to work full time on an 18 month Knowledge Transfer Partnership (KTP) to embed a machine learning capability within AccessPay 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

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 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 also has 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, implementing, 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 perspectives 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 exelling at storytelling through analysis of data
  • Enthusiastic and self-motivated
  • Capable of communicating complex concepts in a clear manner to a wide-ranging audiences

Benefits

  • £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 registeron a higher degree (at a reduced or no cost)
  • Opportunity of a permanent position with the company



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