Data Scientist

About Cint: Cint is a global pioneer and trailblazer in Research Technology (ResTech), feeding the world’s curiosity through an elite programmatic digital consumer insights platform. Representing the world’s largest consumer panel marketplace, Cint provides enterprise clients with instant, consent-based access to nearly 300 million respondents spanning over 150 countries. Our technology unifies data streams from key historic acquisitions—including US-based Lucid and the DACH region’s GapFish—allowing brands to confidently evaluate digital advertising effectiveness, craft market attribution strategies, and capture actionable business intelligence at massive scale.

Position Overview

We are seeking a highly autonomous, analytically sharp, and methodologically strong Data Scientist to join our centralized Data Science & Analytics team under a permanent, full-time remote configuration open across the United Kingdom. In this core quantitative seat, you will work directly on the discovery, development, and validation phases of our advanced Media Measurement and Data Solutions product lines. Shifting completely away from routine non-regulated administrative data transcription filing, generic text copywriting loops, or standalone frontend visual web template styling, you will run an active statistical modeling, algorithmic prototyping, and big data validation laboratory. Partnering cross-functionally with embedded Product and Engineering squads, you will translate abstract marketing metrics into defensible, high-performance machine learning codebases. This position requires a data science authority with 2+ years of professional history who maps information patterns fluidly natively using Data Scientist and statistical methodologies, extracts and manipulates massive data lakes efficiently inside Python ecosystems, tests experimental cohorts against strict sampling hypotheses, and engineers reliable predictive models to validate market signals.

Key Responsibilities

• Media Measurement Model Governance: Design, prototype, and maintain predictive and explanatory statistical frameworks tailored explicitly to media measurement and digital attribution pipelines natively utilizing Data Scientist tools.
• Large-Scale Data Diagnostics: Analyze, clean, and interpret massive, highly distributed datasets independently to identify latent search patterns, demographic consumer trends, and performance insights.
• Statistical Hypothesis Validation: Formulate and run rigorous experimental evaluations, hypothesis matchings, parametric/non-parametric tests, and sample distribution calibrations to ensure the accuracy of platform datasets.
• Machine Learning Pipeline Development: Build, validate, and maintain scalable machine learning methodologies—incorporating clustering, regression, and tree-based classification models—while carefully weighing real-world tradeoffs.
• Ad-Hoc Strategic Discovery: Respond to complex, high-priority client-specific analytical requests, performing custom data manipulation operations and compiling rapid summary results for business stakeholders.
• Cross-Functional Product Syncing: Collaborate directly with distributed software engineers and product managers to map core analytics capabilities cleanly into consumer-facing production architectures.
• Insight Visualization & Presentation: Construct clear, high-impact graphical metrics and strategic data narratives to communicate complex data science results effectively to both technical and non-technical executive teams.

Required Skills & Qualifications

• Possess a formal Master’s degree or equivalent quantitative postgraduate credential in Statistics, Data Science, Quantitative Sciences, Operations Research, or a highly relevant mathematical stream.
• A minimum of 2+ years of proven, successful professional history operating inside a Data Scientist, Quantitative Research Engineer, Analytics Specialist, or closely matching tech-stack capacity.
• Expert Python Data Science Command: Advanced, production-grade mastery writing clean code for statistical analysis, data manipulation, and machine learning model deployment natively inside the Python toolchain.
• Deep foundational command over mathematical principles, including properties of distributions, sampling theory, experimental survey design, regression modeling, and stochastic simulations.
• Demonstrated capability to process, join, and draw defensible conclusions from highly diverse, large-scale unstructured or structured datasets independently.
• Location Context: Position operates under remote parameters open exclusively to qualified data scientists residing permanently within the United Kingdom .

Preferred Strategic Indicators (Nice to Have)

• Prior experience or direct domain analytical background operating inside the Market Research, AdTech, digital attribution, or media measurement sectors.
• Practical data warehousing or query optimization history utilizing SQL statements to pull information from complex structures.
• Exposure to high-scale big data architectures and distributed compute frameworks such as Apache Spark, Hadoop, or cloud-based data lakes .
• Historical background managing multivariate testing, online survey design optimization, or programmatic consumer panel tracking.

What We Offer

• Top-Tier UK Data Science Compensation: A market-competitive annual base salary structure calibrated precisely to your quantitative history, supplemented by attractive corporate stock opportunities and a comprehensive rewards package.
• 100% remote workspace infrastructure freedom from your home office setup anywhere inside the United Kingdom, eliminating corporate commuting boundaries.
• High-Impact Global Scaling Credentials: Unique professional growth milestones achieved by directing the predictive algorithms and validation logic powering the world’s largest consumer panel platform.
• Comprehensive health care preservation benefits protecting employees, providing premium medical, dental, and vision network coverage.
• Access to a recognized industry leader celebrated in Newsweek’s Global Top 100 Most Loved Workplaces, offering robust continuous learning avenues and a transparent culture built on curiosity and cross-border innovation.