Statistical Programmer

Background & Project ContextMeshh sensors passively listen to Wi-Fi-pings from smartphones and we process the raw data to generate visitor footfall, dwell time and other movement metrics in a privacy-preserving manner. Meshh has, over the years, developed proprietary machine learning algorithms to reliably extract event metrics from raw wifi data. With the latest generation of our solution, we are seeking an experienced data scientist or applied ML engineer to further build upon this work. This is a well-scoped project with clear deliverables for a skilled contractor to deliver.Role OverviewWe are seeking an experienced and highly skilled Data Scientist / Applied ML Engineer to lead the implementation and experimentation work for Meshh's Wi-Fi analytics project. This is not a greenfield build - you will inherit a working ML pipeline and evaluation system and be expected to understand it quickly, identify its limitations, and make targeted improvements that meaningfully improve accuracy and robustness.You will work closely with Product Engineering, Analytics and Business stakeholders to diagnose data artefacts, run experiments, and provide clear, defensible improvements that can be deployed to production. Strong statistical reasoning and ML foundations and a pragmatic, outcome-driven mindset are essential.The role is fully remote and open to global candidates.Key ResponsibilitiesAnalysis & ResearchAnalyse Meshh's existing Wi-Fi event datasets to identify randomisation patterns, biases, and data loss characteristicsDesign and develop contextual evaluation methodologies in order to define what good looks like.Evaluate and test promising approaches from recent research and industry practice.Modelling & MitigationDesign statistical or probabilistic models Validate models using historical data, controlled assumptions, or synthetic datasetsValidation & CommunicationClearly document assumptions, limitations, and confidence intervalsProduce concise, decision-oriented outputs for product and leadership stakeholdersAdvise on which approaches are suitable for production vs. reporting-only usageCollaboration & HandoverWork closely with Meshh's engineering and analytics teams to ensure feasibilityProvide working implementation of pipelines, production-ready code and evaluation harnesses/dashboards.Support knowledge transfer as solutions move toward productionRequired Experience & SkillsEssentialStrong foundations in applied data science and machine learningExperience working with large, noisy and incomplete datasetsProficiency in Python (NumPy, pandas, SciPy, scikit-learn or equivalent)Strong statistical reasoning and ability to defend methodological choicesExperience turning analytical work into practical, business-relevant outcomesHighly structured and methodical way of working with clear communication, documentation etc.Highly DesirableFamiliarity with AWS services (Sagemaker, ECS, Fargate) and cloud-deployed ML pipelinesExperience with GPU-accelerated data processingExperience building production-grade unsupervised learning on medium-dimensional structured data.About MeshhMeshh specialises in the metrics of movement and we help our clients understand how people move around and interact within their event spaces. Our technology is based on passive WiFi analytics, offering a scalable, low-cost, and privacy-preserving way to measure and analyse crowd behaviour in physical spaces. Meshh works with high-profile global brands across Retail, Sports and Gaming, Entertainment, Sponsorship, and many other verticals. Similar jobs, Job Purpose The Associate Director, Wearable Sensor Technology is responsible for the design, evaluation, and integration of advanced digital sensor technologies into GSK's clinical programs. This role blends technical depth and strategic insight to advance our next..., An engineering consultancy in the UK seeks a Statistical Lead to oversee statistical methodologies in water industry projects. In this role, you will support data analysts, audit statistical processes, and ensure adherence to reporting standards. Ideal candidates will have...

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