Data Scientist - Iwocapay

Details of the offer

Data Scientist - iwocaPayHybrid in London or Leeds, UK
We're looking for a Data Scientist to join our iwocaPay team iwocaPay works with sellers to offer innovative Trade Credit and Buy-Now-Pay-Later (BNPL) solutions for business customers.
Those sellers are essential to our success, as we need them to offer iwocaPay to their customers, who in turn use us to spread the cost of their purchase, for improving cash flow and operational flexibility.
The company Fast, flexible finance empowers small businesses to manage their cash flow better and seize opportunities - making their business and the economy stronger as a whole.
At iwoca, we do just that.
We help businesses get the funds they need, when they need it, often within minutes.
We've already made several billion in funding available to more than 100,000 businesses since we launched in 2012, and positioned ourselves as a leading Fintech in Europe.
Our mission is to finance one million businesses.
We'll get there by continuing to make our finance ever more relevant and accessible to more businesses by combining cutting-edge technology, data science, and a 5-star customer service.
The function iwoca's Data Scientists specialise in Supervised Machine Learning, Statistical Inference and Exploratory Statistics, focusing on tabular and time series data.
Our work emphasises quantitative predictions through the analysis of conditional probabilities and expectations, using medium-sized datasets.
The role As a Data Scientist within our iwocaPay team, you'll develop insights for our credit assessment model.
Your role will involve evaluating the model, identifying key areas for improvement, and using untapped features to enhance accuracy and reduce friction for customers.
You'll work collaboratively with Data Scientists across iwoca, sharing expertise with a peer group to deepen your knowledge and apply it to iwocaPay.
Additionally, you'll design and analyse tests and build statistical models to help us reach a broader range of customers.
The requirements Essential:
Strong problem-solving skills in probability and statistics, ideally from a quantitative background (e.g., Engineering, Mathematics, Physics, or similar fields).
Proficiency with data manipulation and modelling tools, e.g.
pandas, statsmodels, R.
Experience with scientific computing and tooling, e.g.
NumPy, SciPy, R, Matlab, Mathematica, BLAS.
Self-starter with ability to work autonomously and efficiently manage projects end-to-end.
Excellent communication skills, with the ability to adjust your communication style and technical detail based on the audience.
Bonus:
Experience building machine learning models from scratch (e.g.
creating custom optimisers)
Advanced knowledge of stochastic processes and related mathematical techniques.
Experience with Bayesian analysis.
Experience with Python (our primary programming language).
Knowledge of financial concepts (e.g.
calculations with deterministic cash flows).
The salary We expect to pay from £70,000 - £100,000 for this role.
But, we're open-minded, so definitely include your salary goals with your application.
The culture At iwoca, we prioritise a culture of learning, growth, and support, and invest in the professional development of our team members.
We value thought and skill diversity, and encourage you to explore new areas of interest to help us innovate and improve our products and services.
The benefits Flexible working.
Medical insurance from Vitality, including discounted gym membership
A private GP service (separate from Vitality) for you, your partner, and your dependents.
25 days' holiday, an extra day off for your birthday, the option to buy or sell an additional five days of annual leave, and unlimited unpaid leave.
A one-month, fully paid sabbatical after four years.
Instant access to emotional and mental health support.
3% Pension contributions and share options.
Generous parental leave and a nursery tax benefit scheme to help you save money.
Cycle-to-work scheme and electric car scheme.

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Nominal Salary: To be agreed

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