Data Scientist

Data Science, Financial Data, Python, Building Models

Who are we and why do we do what we do?

We are a data and payments company on a mission! We’re a group of developers, financial experts, and optimists who share a vision for improving the financial wellness of people, their businesses, and their communities.

We started this company with the aim of changing how the industry used and viewed data. As architects of Open Banking, Open Finance, and Open Data, we strive to be a force for good — changing the status quo of how businesses interact with people. We strive to serve the whole population through every change in their finances.

We do this by powering businesses through our APIs and Personal Finance Tech solutions as well as our own personal financial management app for consumers.

We can only do that by being an inclusive and diverse organisation. We invest in our people, and enjoy an environment focused on innovation, collaboration and openness.

What do we offer?

We champion flexibility, and we trust and respect our employees to deliver results in a way that best suits them, working around their own lives and commitments.

We live and breathe a fantastic culture of remote working and you may perform your duties predominantly from your home. However, the heart of Moneyhub is in Bristol and from time to time you will be required to attend company meet ups. Your role may require you to attend client meetings, networking events or group training sessions. You may also be required to work at such other place or places as we may reasonably require from time to time. As a minimum, you will be required to attend a quarterly All Team Away Day at a location of our choice (including overseas).

As well as a truly flexible approach, we also offer a fantastic range of benefits, including:

Remote working - with quarterly away days, regular team meeting and face to face client meetings as required.

10% contribution towards your Pension from your very first day with us;

25 days of holiday (plus bank hols), rising to 30 days after two years;

Choose to take your entitlement to UK bank holidays at other times based on your own days of significance;

Private medical insurance, including cover for pre-existing conditions, plus dental and optical benefit;

Six week Moneyhubber Family Pay when you become a new parent;

Permanent health insurance and life cover - much greater than the industry standard (death in service);

Employee assistance programme;

Professional development support, with dedicated allowance of time and money;

Life event leave;

Cycle to work scheme;

EV Salary sacrifice scheme;

£750 towards professional memberships

Remote working benefits, including work from almost anywhere, access to co-working spaces and support for your home office set-up

High spec laptop

Requirements

Sounds great right? What will you be doing?

The Data Science capability at Moneyhub generates value from a rich repository of data sources.  Its function is critical to the day-to-day running of the business, and it also spearheads the development of novel ideas to exploit new commercial opportunities.

You can expect to work on:

• The Machine Learning based transaction categorisation engine that underpins the budgeting capabilities of the app and affordability checking, e.g. for mortgages.
• Data Science oriented product development: identifying improvements to existing algorithms, or the creation of new products using a quantitative approach. Examples: identifying regular payments from raw transaction data, geotagging banking transactions from their descriptions, reverse engineering credit card APRs.
• Analysis of the characteristics of the user base, i.e. user segmentation, to support business decisions.  For instance, what’s the current financial situation of the users of the app, what are their goals and how should the product be developed to aid the user in their journey?
  
  
  About you
  
  
• Have 3 or more years of experience as a Data Scientist.
• Be fluent writing code in Python and queries in SQL.  We use Jupyter notebooks, Python and Pandas frequently for analysis.
• Understand a wide range of classification/regression algorithms and have applied them using libraries such as scikit-learn.
• Have implemented regression analysis and understand how to interpret the results.  More generally, you know how to apply appropriate statistical techniques to assess the significance of results.#
• Be able to take raw data sets and build narratives around insights you discover; 
• Be comfortable using a variety of approaches to present results in a way a non-technical audience can understand and act on.
• Appreciate the importance of data cleaning and have previously applied a range of approaches to achieve this.
• Have a bachelor's or master’s degree in a numerical or engineering subject such as Data Science, Mathematics or Computer Science.