Senior Data Scientist
About Codat
Codat is an advisory intelligence solution purpose-built for modern commercial banking. Through rich, specialized data, forward-looking insights, and integrated workflows, Codat empowers banking teams to deepen their relationships, grow their revenue, and simplify their day-to-day work.
Founded in 2017 and backed by JPMorgan, PayPal, Amex, Plaid, and Shopify, Codat has successfully powered over 350,000 connections to business customers’ financial systems — and is trusted by industry leaders to turn scattered information into actionable, strategic advantages in real time, every time.
Who are we looking for?
We are looking for a highly experienced and product-focused Senior Data Scientist to join our team. You will be both a leading individual contributor and a mentor, taking ownership of the delivery of high-quality, data-driven solutions to some of our most complex and ambiguous challenges. You’ll make a significant contribution to our data science stack and capability, helping to foster a team culture of technical excellence and continuous improvement. You will also play a key role in our broader engineering culture, defining best practices, collaborating with other senior engineers, and fostering a mindset of continuous improvement.
The Role
As a Senior Data Scientist at Codat you will:
Technical Leader & Mentor: Drive excellence and foster a culture of continuous improvement. You'll act as a mentor to other team members, sharing expertise and guiding them in best practices for design, development, and support of products, while also leading technical initiatives and contributing to architectural decisions.
Domain-Oriented Problem Solving: Go beyond the technical spec to develop a deep empathy for our clients by understanding the nuances of their connected financial data. You will use this domain expertise to contribute to product strategy, challenge assumptions, and architect solutions that deliver genuinely actionable intelligence.
AI-Driven Innovation: Proactively identify and prototype opportunities to embed AI/ML into our core products, leveling up our offering and delivering novel value to clients. Alongside this, you will champion the use of AI tools to streamline engineering operations, enhance productivity, and upskill others across the team.
AI-Driven Execution: Leverage AI to maximize productivity, be that in conducting research, gathering information or building solutions
Be action-oriented: Eager to take ownership of large-scale projects, championing ideas from discovery all the way to production
Strategic & Tactical: Balancing big-picture vision with hands-on delivery. Threading the needle between speed and quality, delivering value quickly whilst building firm foundations for our products and in-house capabilities
Communication: Communicate technical concepts effectively to both technical and non-technical team members.
Be a team player who loves working within a multidisciplinary team and collaborating with other teams
Codat's values - It is important that all team members live by our values.
Stay Curious:
We seek to understand 'The Why?' to make better decisions.Progress over polish:
We move things forward, make clear calls, and learn through action and feedbackOwn it
We challenge openly, commit fully and take responsibility for outcomes- good and bad
What you’ll bring to the team
A broad and deep understanding of a wide range of data science techniques, including classic ML, deep learning and cutting edge AI & Agents, honed through extensive practical experience across a range of domains
Expert-level proficiency in Python and its data science ecosystem (e.g., scikit-learn, pandas), with the ability to select the right tools for complex problems and set technical standards for the team
Advanced, hands-on expertise in SQL and big data platformslike Databricks, used for sophisticated data manipulation, feature engineering, and optimizing complex data workflows
Extensive, proven experience in MLOps: owning the end-to-end lifecycle of production models, including designing scalable and reliable deployment strategies (e.g., serverless applications, containerization) and model monitoring, observability and continuous improvement
Strong proficiency in designing and utilizing CI/CD pipelines and cloud infrastructure (e.g., AWS, GCP, Azure) to automate and streamline the delivery of data science products, with an appreciation of wider software development best practices and how they can be applied
Exceptional ability to articulate complex data science concepts and solutions, influencing both technical and non-technical stakeholders and shaping product strategy through clear, data-driven narratives
An intuitive understanding why data protection and security practices are important
A degree in a STEM subject