Credit Analytics and Data Science Lead

A fully remote role based anywhere in Poland or the UK

Role Overview 🚀

The Credit Analytics & Data Science Lead has the responsibility for driving credit risk strategy innovations by leveraging cutting edge data science / machine learning approaches. This role is ideal for someone who is equally excited about achieving commercial outcomes as they are about analytics, but we are wide open to what this may look like. We know that incredible data science / ML talent comes in all shapes and sizes.

This role reports into the Chief Risk Officer and will work closely with the product and credit underwriting teams to own delivery across the following areas: credit risk ML models, acquisition strategies and portfolio analytics.

About Uncapped 👫

Founded in 2019, Uncapped is the fastest, most affordable way for growing online businesses to fund marketing and inventory. The company was born out of frustration with the limited financing options available for UK and European entrepreneurs to finance growth.

Uncapped provides business advances of between £10k and £5m without credit checks, personal guarantees, warrants, equity, or compounding interest -- and makes money by charging a low flat fee that is paid back from future sales revenue. Uncapped has raised $120 million from investors including Lakestar, Mouro Capital, Global Founders Capital, White Star Capital, Seedcamp, and All Iron Ventures.

What you will do ✍️

• Use advanced machine learning / statistical techniques on standard and non-standard data sources to build best-in-class credit risk models and strategies - we are building some truly awesome and innovative financial products that require creative thinkers who can develop deep understanding our product, identify appropropriate data and modelling strategies and test and learn different approaches to credit strategy / modelling
• Work closely with the commercial teams to ensure credit risk requirements are considered while building new propositions and introducing new channels / segments
• Continuously improve business performance tracking to drive timely identification of emerging risks and incorporate learnings to improve credit strategies

Requirements

Who you are 👀

• Hands-on statistical / machine learning modelling expert with a strong desire to own and influence credit strategy
• Educated to a degree level or above in a STEM discipline - very strong theoretical foundations in statistics / machine learning disciplines is a must
• Very strong coding skills, having utilised Python / R / SQL on a daily basis - you will write your own codes, supervise analysts and engage with data engineers on a daily basis
• Ability to think from first principles - we are looking for confident technicians who are not afraid to tackle new problems and try out new and untested approaches
• Robust understanding of credit risk strategy development processes a must - hands on experience as a strategy manager is a plus
• Entrepreneurial mindset - you should constantly look to make the most of available opportunities, overcome quickly to learn from setbacks, be decisive and have a bias towards action
• Confident communicator, with demonstrable experience of having delivered insight and strategy to a variety of both technical and non-technical stakeholders

Benefits

What we offer 🎁

At Uncapped, our people make us successful. We are a startup with big goals and we work hard, so we want to give everyone the benefits they really want. We’re continually adding to this list as new people join -- here are some of the things you can expect:

• A competitive compensation plan
• Flexible working: Remote-first processes for ultimate flexibility
• Unlimited time off to relax and see the world
• A personal growth fund that helps you to become your best
• Weekly recognition and awards that celebrate all the wins, both big and small
• Annual retreat abroad to hang out with your team on a paid staff trip
• A workspace just for you, with £350/€400 to optimise your home office

We can only consider applications from those who are eligible to work, without sponsorship, in the location to which they have applied.