Applied Scientist (Machine Learning)

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, data scientists, data analysts, and product managers building and evolving ML models, real-time systems, reports, and performing deep analysis of pricing, retention, and offer strategies.

Working with advanced predictive models, MLOps best practices, and scalable software systems, you will implement and evolve intelligent solutions to align Teya with the success of our customers.

In this role, you’ll be:

• Helping Teya to use data to drive business decisions by implementing and continuously improving through experimentation advanced machine learning models.

• Working on projects including but not limited to customer lifetime value, churn propensity, forecasting, risk, cost-to-serve and cost-to-acquire modelling

• Building predictive models to a production level adopting best practices for coding, deployment, monitoring, and experimentation.

Qualifications

Your Story

• Background in a quantitative field (Computer Science, Mathematics, Machine Learning, AI, Statistics, Economics or equivalent)

• 5+ years of professional working experience

• Someone who thrives in the incremental delivery of high quality production systems

• Proficiency in Java, Python, 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 model objectives and performance to business stakeholders

• Strong analytical and problem-solving skills

• Ability to think creatively and insightfully about business problems

• Nice to have:

  • Proficiency with Snowflake

  • Proficiency with Amazon SageMaker

  • Proficiency with Docker and Kubernetes

Additional Information

The Perks

• Competitive salary;

• Health Insurance;

• 25 days of Annual leave (+ Bank holidays);

• Flexible working hours, as long it suits both you and your team.