|Job Type:||Full Time|
We’re looking for talented, experienced Data Scientists to join our growing Data Science team.
GoCardless is building a new global network for recurring payments. We’re cutting out the intermediaries and linking together direct debit schemes from around the world to create a simple way of collecting payments directly from customers’ bank accounts.
Annual payment volume at GoCardless exceeds £10 billion, and we’re processing up to hundreds of thousands of transactions every day. As we grow, there is an increasing demand for data across all functions. Our ability to understand our users, and make sound and timely business decisions is hugely dependent our ability to extract meaning from this data.
The data team exists to enable GoCardless to make smarter decisions by providing a combination of data oriented products, solutions and technologies. The data team is building data products that massively enhance the value and differentiation of our products at a global scale.
Data is at the centre of our ability to add value to our customers and to our business. Fraud prevention, marketing optimisation, churn prevention, payment failure optimisation and upsell propensity are all areas of active work. You will work closely with cross-discipline teams, taking responsibility for end to end algorithm development from initial concept through to production readiness.
- We are looking for Data Scientists that will help us build predictive models that substantially increase the value that we provide to our customers and the efficiency of our operations by building new data products that leverage our growing volume of raw data.
- You will develop and productionise data products and predictive models in collaboration with your team. Working with Product Managers, software developers and subject matter experts, you’ll bring your own expertise to the team helping to move from abstract concepts, through design and build, to scalable algorithms that will deliver the teams goals.
- You will play a part in specifying the underlying infrastructure we are building to support the day to day work of Data Scientists and to enable models to be easily productionised.
- You will create meaningful data visualizations that communicate your findings and relate them back to how your insights create business impact.
WHO WE’RE LOOKING FOR
- You’re a self-starter - you can independently drive a team's work forward, finding and exploiting opportunities where data science can impact our business objectives.
- You love digging into data and uncovering actionable insights
- You’re passionate about using data to help teams become more productive
- You have intellectual curiosity
- You are a communicative person that values building strong relationships with colleagues and stakeholders and have the ability to explain complex topics in simple terms.
- You’ll have a degree (or PhD) in a numerate discipline or significant experience working in a commercial setting.
- You'll have commercial experience building robust ML models in a production environment.
- You have strong technical skills in data manipulation and extraction (strong SQL knowledge), data analysis and model prototyping and productionisation (Python, R or similar)
- You have solid experience of deep-dive analysis of complex data; managing data quality; feature selection; and prototyping, validation, and productionisation of Machine Learning models.
- You’ll be able to point to a range of different techniques to solve a problem and demonstrate how you're able to apply maths to data and deliver tangible, quantifiable business results.
- You have strong data visualisation skills
- You're comfortable with writing and maintaining code. Our preference is that your language of choice for data-wrangling is Python. However, R is also an option during prototyping phases
- You have a track record of deploying predictive models and data products in production with quantifiable impact.
Our team come from a variety of backgrounds and we welcome diversity – if you’re unsure, please apply.
Reference - #LI-TW1