Applied Machine Learning Scientist

About the role

This position advances machine learning methods for advertising technology within a remote-first engineering team. The role emphasizes research, prototyping, and production to impact campaign performance at scale. Work is available across Ireland, the UK, and Germany under a remote-first policy. The position requires a high degree of ownership over the end-to-end lifecycle of machine learning initiatives, from initial ideation through deployment and monitoring in production environments. You will partner closely with product managers and engineers to translate ambiguous business goals into concrete, testable machine learning solutions. A significant portion of the role involves diagnosing performance issues and designing experiments to validate the effectiveness of new algorithmic approaches. Success in this role is measured by the tangible improvement of advertising ROI and the robustness of the systems that deliver it.

Key facts

What you'll do

Systematically investigate and refactor existing machine learning pipelines to eliminate bottlenecks and improve scalability in digital advertising systems. Design and implement experiments that measure the incremental impact of algorithmic changes on key performance indicators. Collaborate with data engineers to ensure that feature stores and data pipelines support the requirements of advanced modeling techniques. Develop prototypes using historical campaign data to validate hypotheses before committing to large-scale engineering efforts. Translate complex model outputs into clear narratives and visualizations for non-technical stakeholders to support strategic decisions. Conduct rigorous error analysis to identify patterns in model failures and drive the development of targeted improvements. Evaluate and compare different machine learning frameworks and libraries to select the most appropriate tools for production constraints. Partner with product teams to define success metrics and establish monitoring dashboards for deployed models. Continuously research state-of-the-art methods in representation learning and causal inference relevant to advertising measurement. Ensure that all technical deliverables adhere to the company's standards for code quality, documentation, and reproducibility.

Requirements

A master's degree or PhD in computer science, statistics, operations research, or a related field is mandatory, with dual degrees treated as an advantage. Demonstrated expertise in statistical modeling and optimization is required to navigate the complexity of advertising datasets. Candidates must possess strong coding skills in data structures and algorithms to build efficient and reliable software. The ability to break down complex, open-ended assignments into concrete, actionable steps is essential for clear solution paths. Deep knowledge of machine learning theory and practice is a non-negotiable hard bar for this position. Excellent written and verbal communication skills are required to align with a friendly, cooperative team environment. Proven experience working with large-scale data in distributed systems is highly valued. The role demands comfort with ambiguity and the discipline to deliver results in a fast-paced, iterative environment. A portfolio of past analyses and projects is considered a strong indicator of capability, often outweighing formal credentials.

Nice to have

Experience with specific technologies relevant to advertising technology, such as streaming data platforms and recommendation systems. Familiarity with causal inference methods for measuring campaign lift and attribution. Contributions to open-source machine learning projects or publications in relevant academic venues.

Practical notes

The role operates under a remote-first policy, with candidates eligible for locations across the UK, Ireland, and Germany.

Questions to ask

Worth asking in any interview: how the team measures success, who the role works with daily, what the onboarding looks like, and what the company is trying to achieve this year. Asking what past hires did well is a strong final question. Keep the list short and pick the questions that matter most to you.

Career growth

Data careers grow toward senior analyst, staff data scientist, or data engineering lead. Many professionals specialize in machine learning, analytics, or infrastructure. Cross-functional work with product and engineering teams becomes more important at senior levels. The field changes quickly, so continuous learning is part of the job. Professionals who can translate numbers into decisions tend to advance fastest.

About the company

StackAdapt is hiring for Applied Machine Learning Scientist. Ireland.