Forward-Deployed Data Scientist II

About the role

You will partner with commercial and product teams to design, validate, and operationalize machine learning models that power high-value marketing experiences. The role demands rigorous ownership of model lifecycles, from problem framing through deployment and ongoing monitoring in production. You will translate ambiguous business goals into testable hypotheses and reliable data products that directly influence customer outcomes. A core part of the job is simplifying complexity for non-technical stakeholders while maintaining scientific integrity in your methods. You will act as a technical ambassador for the data team, ensuring that models are explainable, robust, and aligned with business constraints. Success in this position requires comfort with fast priorities, clear documentation, and a proactive approach to identifying risks before they impact customers. You will contribute to building internal capabilities and playbooks so that data-driven decisions scale across the organization and markets.

Key facts

What you'll do

Design and implement machine learning models that drive personalized marketing journeys and measure their business impact.

Perform advanced statistical analysis and experimentation to evaluate model performance and inform strategic decisions.

Collaborate with data engineers to optimize data pipelines, ensuring datasets are reliable, timely, and well-documented for modeling.

Lead scoping sessions with clients and internal stakeholders to clarify requirements and translate business needs into analytical plans.

Develop and maintain scalable prediction systems that operate reliably in production environments and meet strict performance standards.

Conduct rigorous model validation, diagnose failure modes, and iterate on solutions to improve accuracy and generalization.

Communicate insights and model behavior to diverse audiences using clear visualizations, narratives, and technical summaries.

Partner with product teams to run controlled experiments, interpret results, and guide data-driven product decisions.

Stay fluent with modern data stacks, machine learning platforms, and MLOps tooling to support continuous model improvement.

Contribute to internal knowledge sharing, code reviews, and best practices that elevate the quality and maintainability of data work.

Requirements

You hold a Bachelor's degree in Computer Science, Data Science, Mathematics, Engineering, or a related field, with a Master's or PhD in a relevant technical discipline preferred.

You bring 3-5+ years of hands-on experience as a Data Scientist, Machine Learning Engineer, or similar role working with large-scale data and production environments, with customer-facing or consulting experience strongly preferred.

You are proficient in Python (Pandas) and core machine learning libraries such as TensorFlow, Keras, scikit-learn, CatBoost, and XGBoost, and skilled in SQL for querying and manipulating datasets.

You write well-structured, modular, documented code and follow strong development practices including Git, CI/CD, testing frameworks, type-hinting, and code reviews to build scalable, maintainable solutions.

You are comfortable working directly with clients and cross-functional teams, aligning stakeholders and translating technical concepts into clear business value.

You identify opportunities and risks early, troubleshoot obstacles, and drive creative solutions as an entrepreneurial problem-solver.

You stay current with industry trends, explore new tools and technologies, and thrive in environments that push you to grow as a continuous learner.

You explain complex technical ideas persuasively to both technical and non-technical audiences as a clear communicator.

Practical notes

This role is based in London and involves customer-facing work with travel and collaboration across time zones.

You will need to meet the listed education, experience, and technical requirements.

Typical interview steps

Data interviews commonly include a SQL or coding exercise, a statistics question, and a case study. Candidates may be asked to design a metric, interpret an experiment, or build a small model. Some companies give a take-home analysis. Expect questions about past projects and the business impact of your work. Interviewers often evaluate how you communicate uncertainty and business impact, not only the math. Bringing a clean write-up of a past analysis to the interview is well received.

Good to know

The role works with machine learning platforms, personalization engines, and modern data stacks used in production marketing environments.

Data scientists in this role often transition into solution design and product influence.

The team emphasizes rigorous experimentation, model reliability, and measurable business outcomes.

Collaboration spans product, engineering, and client stakeholders across global markets.

Machine learning model deployment and MLOps practices are central to daily work.

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.