You'll help build the bank of the future by super charging user engagement in 2025. Lead the data strategy and continue to evolve Monzo for customers to make it magically simple to manage money day-to-day. Develop and execute best practices for experimentation enabling the team to make data-informed decisions. Work with leaders to build and iterate on product ideas.

As a Machine Learning Engineer at Datatonic, you'll engineer code in Python and develop production-grade software for machine learning and data-driven solutions. Responsibilities include developing ML solutions, participating in project scoping, optimizing machine learning solutions for performance/scalability, and ensuring efficient data flow between databases and backend systems.

A Lead Machine Learning Scientist at Monzo is a technical Individual Contributor (IC) leadership position, developing models that make every customer interaction more relevant and timely. The role involves working with billions of rows of data on a modern cloud native data platform, leveraging experience to develop and deploy advanced Machine Learning models and improve customer outcomes.

The Data Product Owner will be responsible for maximizing the value of data by identifying and prioritizing features for the data team. This role will understand the needs, strategic objectives, and industry norms of all MeridianLink’s business processes and data products to clearly express feature requests. The role will coordinate and communicate across technical, business, and customer-facing teams.

Drive innovation in how Life360 experiments, segments, and optimizes its user experience to maximize growth and retention. This individual will be at the forefront of developing segmentation models, data-driven growth strategies, and experimentation hypotheses. Your expertise will help Life360 make smarter, faster, and more scalable decisions, directly impacting millions of users.

Inform PM, Design, and Strategic Leadership decisions via ad-hoc analysis, interactive dashboard creation, and evaluation of live operations performance. Design repeatable measurement frameworks to evaluate game systems over time and inform updates for future releases. Develop models for predicting and segmenting player behavior and quantify opportunities for new initiatives.

Drive NLP and machine learning projects and be responsible for developing methodology and solutions to support technical, analytical, and operational requirements. Design and implement machine learning algorithms for NLP/LLMs or deep learning models at scale to meet business requirements.

Work with technology companies to identify improvements of existing technology, organizations, processes, and tools used to develop software products and services. Work with private equity companies and other investment firms to help them understand the technology strengths, risks, and opportunities for improvement for potential investments. Design and implement solutions from scratch.

Kalepa is seeking a Machine Learning Engineer with 3+ years of experience to lead the framing, development, and deployment at the scale of machine learning models. As a Machine Learning Engineer, you will lead the framing, development, and deployment at scale of machine learning models to understand the risk of various classes of businesses. You will be turning vast amounts of structured and unstructured data from many sources into novel insights about behavior and risk.

We are seeking a data science specialist to drive innovation, explore new techniques, and implement machine learning algorithms to solve complex business problems, requiring motivation and resilience.