Background & Project Context
Meshh 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 Overview
We 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 Responsibilities
Analysis & Research
- Analyse Meshh’s existing Wi-Fi event datasets to identify randomisation patterns, biases, and data loss characteristics
- Design 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 & Mitigation
- Design statistical or probabilistic models
- Validate models using historical data, controlled assumptions, or synthetic datasets
Validation & Communication
- Clearly document assumptions, limitations, and confidence intervals
- Produce concise, decision-oriented outputs for product and leadership stakeholders
- Advise on which approaches are suitable for production vs. reporting-only usage
Collaboration & Handover
- Work closely with Meshh’s engineering and analytics teams to ensure feasibility
- Provide working implementation of pipelines, production-ready code and evaluation harnesses/dashboards.
- Support knowledge transfer as solutions move toward production
Required Experience & Skills
Essential
- Strong foundations in applied data science and machine learning
- Experience working with large, noisy and incomplete datasets
- Proficiency in Python (NumPy, pandas, SciPy, scikit-learn or equivalent)
- Strong statistical reasoning and ability to defend methodological choices
- Experience turning analytical work into practical, business-relevant outcomes
- Highly structured and methodical way of working with clear communication, documentation etc.
Highly Desirable
- Familiarity with AWS services (Sagemaker, ECS, Fargate) and cloud-deployed ML pipelines
- Experience with GPU-accelerated data processing
- Experience building production-grade unsupervised learning on medium-dimensional structured data.
About Meshh
Meshh 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.
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