Data Scientist (Mid and Senior) - Fraud and Risk Evaluation

Company Description

Hello! We're Teya.
Teya is a payment and software service provider, headquartered in London serving small, local businesses across Europe. Founded in 2019, we build easy to use, integrated tools that enable our members to accept payments and boost business performance.

At Teya we believe small, local businesses are the lifeblood of our communities.
We’re here because we don’t believe there’s a level playing field that gives small businesses with a fighting chance against the giants of the high street.

We’re here because we see banks and legacy service providers making things harder for them. We don’t think the best technology or the best service should be reserved for those with the biggest headquarters.

We’re here to fight for a future where small, local businesses can thrive, and to commit the same dedication they offer all of us.

Become a part of our story.
We’re looking for exceptional talent to join our mission. We offer a chance to create impact in a high-energy and connected culture, while benefiting from continuous learning opportunities, a supportive community which is proud to serve our mission, and comprehensive benefits.

Job Description

Your Mission

You will be part of a joint team of machine learning engineers and data scientists building and evolving ML models, real-time systems, reports, and deep analysis of fraud detection and mitigation activities to protect merchants, their customers, and Teya from illicit activities.

Working with advanced predictive models and scalable software systems, build and grow intelligent solutions to reduce all kinds of risk and allow Teya to focus on effectively serving our merchants.

In this role, you’ll be:

• Helping Teya to use data to drive business decisions
• Working on projects including but not limited to fraud detection, transaction monitoring, customer onboarding risk, cost-to-serve and cost-to-acquire modelling
• Building predictive models to a production level adopting coding best practices
• Working closely with other data scientists and machine learning engineers to support the analytical part of the machine learning lifecycle

Qualifications

Your Story

• Background in a quantitative field (Computer Science, Mathematics, Machine Learning, AI, Statistics, Economics or equivalent)
• 3+ years of professional working experience
• Someone who thrives in developing innovative, state-of-the-art products that can meet and surpass the latest advances in the field
• Proficiency in Python, Amazon SageMaker, SQL, Jupyter Notebook
• Experience with Machine Learning and statistical inference.
• Understanding of ETL processes and data pipelines and ability to work closely with Machine Learning Engineers for product implementation
• Ability to communicate outcomes of a data analysis to business stakeholders
• Strong analytical and problem-solving skills
• Ability to think creatively and insightfully about business problems
• Nice to have:
  • Proficiency in Snowflake.

Additional Information

The Perks

• Competitive salary;
• Health Insurance;
• 25 days of Annual leave (+ Bank holidays);
• Office snacks every day;
• Friendly, comfortable and informal office environment;
• Flexible working hours, as long it suits both you and your team.