Senior Data Scientist

I am currently partnering with an innovative technology company to find a Senior Data Scientist. This is a critical role for an experienced practitioner with a strong quantitative foundation and a proven track record of solving complex measurement and forecasting problems in production.Working closely with my client's leadership, you will own the end-to-end modelling lifecycle — from architecting solutions and translating ambiguous business problems into statistical frameworks, to deploying robust models and communicating strategic insights to the executive team.
My client is looking for someone who brings years of industry experience and a pragmatic approach to data science ( Retail or Ecom) You will need sharp statistical fundamentals, strong engineering habits, and the ability to act as a technical leader within the business—instinctively stress-testing results, establishing best practices, and elevating the capabilities of the wider team.
What You'll DoArchitect and Lead: Design, build, and deploy robust statistical and machine learning models for forecasting demand and core business metrics (e.g., regularised regression, gradient boosting) while ensuring interpretability (e.g., SHAP).Own Causal Inference: Lead the incrementality measurement strategy—designing complex experiments, advanced A/B testing frameworks, matching methods, and synthetic controls.Advanced Forecasting: Implement and scale time-series methods, including Bayesian Structural Time Series (BSTS), to estimate incremental impact and forecast against a credible baseline.Strategic Partnership: Partner closely with the company's business leaders to translate ambiguous goals into well-defined, measurable analytical projects.Technical Excellence: Write production-ready, highly reproducible analysis code. Drive data science best practices across the team, including version control, CI/CD, and MLOps workflows.Stakeholder Management: Communicate complex statistical findings and their strategic implications clearly and persuasively to non-technical executives.Mentorship: Mentor junior team members, guiding them on statistical rigour, engineering standards, and practical problem-solving.
What My Client is Looking ForBackground You’re likely a strong fit if you possess:5+ years of industry experience as a Data Scientist, with a strong focus on forecasting, causal inference, and machine learning.A relevant quantitative degree (e.g., Data Science, Statistics, Mathematics, Computer Science, Econometrics). An advanced degree (Master's or PhD) is highly regarded.
Required SkillsExpert-level Statistics & Probability: Deep expertise in reasoning about uncertainty, experimental design, and hypothesis testing in real-world, noisy environments.Advanced Coding: Expert proficiency in Python and SQL for complex data manipulation, modelling, and productionizing workflows.MLOps & Engineering: Strong understanding of how to put models into production, including monitoring for drift, train/test discipline, and model lifecycle management.Executive Communication: Exceptional written and verbal communication skills—you can explain complex methodologies and their limitations to a non-technical audience with ease.
Strong PlusesA proven track record of implementing advanced causal inference, econometrics, or Bayesian time-series forecasting (e.g., BSTS) in production environments.Extensive experience with modern data stacks (e.g., Snowflake, dbt), cloud platforms (AWS/GCP/Azure), and orchestration tools.Experience leading or scaling a data science function within a fast-paced technology, digital platform, or B2B SaaS environment.
What My Client OffersHigh Impact & Autonomy: Ownership of critical technical capabilities where your work directly drives the company's strategy.Leadership Opportunities: The chance to shape the technical direction of the team and mentor junior data scientists.Technical Collaboration: Work alongside highly experienced data engineers and scientists in a mature data environment.Benefits: A highly competitive salary, dedicated learning and development budget, flexible/hybrid working, generous leave policies, and more.