Data Scientist - Foundation in Manchester

At thinkmoney, we are committed to delivering unparalleled access, equality, and inclusion through innovative financial solutions for those who need them most. We are passionate about driving transformative impact, cultivating trust, and creating enduring value. We are a dynamic, collaborative, and forward-thinking team, where you’ll thrive remotely while shaping the future of financial technology. We foster continuous development, wellbeing, and work-life synergy, all within a culture of transparency, inclusion, and mutual respect.

With innovative technology, exceptional customer service, and an outstanding team of talented thinkers including Tech Gurus, Design Wizards, and Security Experts who drive our progress and deliver financial solutions loved by hundreds of thousands of customers. With top Trustpilot, App Store, and Net Promoter Scores, we know we’re doing something right, but we’re just getting started.

At thinkmoney, we believe our people are our greatest asset. That’s why we create a culture where everyone feels valued, comfortable, and inspired to grow. You’ll have the freedom to thrive in a bold, supportive, and innovative environment where your ideas make an impact, your career is yours to shape, and your well-being is always a priority.

Joining thinkmoney means becoming part of a smart, no-nonsense, best-in-service business with a big ambition to help people. Our team is full of super-talented, passionate, and driven people who are always there to support each other, and they’re a pretty friendly bunch too!

We also offer plenty of thinkmoney perks, like annual paid Charity, Birthday, Wellbeing, and Wedding days, our employee benefits platform YuLife, access to Mental Health First Aiders and a Virtual GP, regular social events, and so much more.

If you’re committed to making a difference to everyday people’s lives, up for a challenge, and want to work with some pretty awesome people along the way, we’d love to hear from you. This is your chance to join a team ready to take things to the next level and make a real difference in people’s lives!

What Makes Us Different?

At thinkmoney, we’re not about old school ways of working. Our people define our culture, and we encourage everyone to be their authentic selves. Diversity and inclusion are at the heart of who we are because we know that different perspectives fuel innovation and success.

• Be Bold. You’ll have the freedom to try new things, learn from mistakes, and make a genuine impact.
• Collaborate. You’ll work with a talented and supportive team that celebrates wins together.
• Thrive. Whether you’re just starting or looking to grow, we’re here to help you build your career your way.

We’re committed to creating a space where everyone feels welcome, valued, and excited to be part of something special.

Role information

As a Data Scientist - Foundation at thinkmoney, you will help turn messy, real world financial data into reliable datasets, clear analytical insight, and early stage machine learning models. You’ll work primarily in Python and SQL, using AWS (including Amazon SageMaker Unified Studio) to explore data, build and test models, and support product alongside our Senior Data Scientist and team of Data Engineers and Analysts. This is an early career role with structured mentorship and support. You’ll gain exposure to the full lifecycle; from data access and feature creation through to model evaluation, deployment patterns, monitoring, and governance, with guided learning.

Key Responsibilities:

• Use Python and SQL to extract, transform, and analyze data from our Data Platform (AWS), using existing datasets and agreed access patterns (and occasional external files/APIs where required).
• Contribute to reusable datasets and features using existing data quality checks, documentation, and agreed versioning patterns (e.g., Git), with guidance and review.
• Perform exploratory data analysis, create clear visualisations, and communicate findings via notebooks and concise write-ups.
• Develop, train, and evaluate baseline machine learning models (e.g., classification/regression) and support experimentation in Amazon SageMaker Unified Studio.
• Support the Senior Data Scientist with model product: work with data engineers to implement agreed deployment, monitoring, and safe rollout approaches (e.g., shadow runs), with guidance and review.
• Follow and contribute to data and model governance: support documentation and traceability (e.g., data lineage notes, model versioning, model cards) and follow privacy by design practices.
• Follow engineering best practice: source control (Git), code review, testing, reproducibility, and working in an Agile workflow.

About you

You are intellectually curious and enjoy getting to the truth in the data, but you also have the pragmatism to work within real world constraints (time, quality, and compliance). You value clean, reproducible work, you communicate clearly, and you’re comfortable learning fast in a collaborative team. You want to build a foundation in data science and learn enough of the surrounding delivery disciplines (data quality, governance, and production practices) to collaborate effectively and ship models safely.

What You’ll Bring

• A degree in a quantitative subject (e.g., Data Science, Computer Science, Statistics, Maths, Physics, Engineering) or equivalent practical capability.
• Early in your data science career, with some hands‑on experience through university, placements, bootcamps, or personal projects.
• Solid Python foundations for data work (e.g., pandas) and basic machine learning (e.g., scikit‑learn), with an emphasis on readable, well‑structured code.
• Solid SQL skills (joins, aggregations) and confidence working with relational data.
• Understanding of core statistics and ML concepts (train/test splits, overfitting, evaluation metrics, bias/variance trade‑offs).
• Clear written and verbal communication; ability to explain analytical outcomes to non‑specialists.
• Appreciation of data quality and traceability (e.g., understanding what good data looks like and how to document assumptions) to collaborate effectively with data engineers and analysts.
• A strong learning mindset for AWS tooling and comfort operating in a regulated environment (security, privacy, governance).
• Comfortable asking questions, taking feedback, and working in a collaborative environment (e.g., pairing, code reviews).

Great if you have:

• Exposure to Amazon SageMaker (Unified Studio, training jobs, Pipelines, Model Registry) through projects, placements, or coursework.
• Git and basic software engineering hygiene (code review, unit tests; CI/CD awareness is a plus).
• Experience producing simple dashboards/visuals (e.g., Power BI) to communicate outcomes.
• Any exposure to financial services data or problem spaces (risk, fraud, affordability, payments).

What we can offer you

As we’ve already said, our people are what matter, and we do a lot when it comes to them. As a thinker this is what you’ll receive:

• Opportunity to work for one of the most exciting FinTech companies in the UK.
• A culture of collaboration and growth so your career can become what you want it to be.
• Hybrid working – balance of remote working and on‑site to suit your work/life balance.
• Company pension.
• 23 days holiday, rising to 28 with service. Plus, all major