Senior Data Scientist

mecs communications

Slough

Hybrid

GBP 70,000 - 110,000

Full time

14 days+

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Job summary

mecscomms is recruiting an experienced Senior Data Scientist to join one of the UK's most advanced data science teams, helping shape the future of customer analytics through cutting‑edge machine learning, statistical modelling & cloud‑based AI solutions.

This hybrid role provides exposure to large‑scale customer data and Azure‑based technologies, with 2 days in the office in Welwyn Garden City or Slough, and collaboration with data engineers and analysts to deliver production‑ready analytics.

Qualifications

Experience as a Senior Data Scientist delivering end-to-end data science solutions.
Strong analytical skills with commercial awareness and problem solving.
Experience with structured and time-series data and production deployment.

Responsibilities

Deliver end-to-end data science solutions from concept to production.
Build machine learning models that improve customer and business outcomes.
Apply statistical techniques to solve complex business problems.
Develop propensity and predictive models for customer decisioning.
Design & optimise time series forecasting models.
Build scalable data pipelines and production-ready analytics.
Collaborate with cross-functional teams to deliver business value.
Monitor, maintain and continuously improve deployed models.
Present analytical insights to technical and business stakeholders.
Promote best practice in data science and software engineering.

Skills

Senior Data Scientist

Data Scientist

Machine Learning

Artificial Intelligence

Predictive Analytics

Statistical Modelling

Time Series Forecasting

Propensity Modelling

Feature Engineering

Data Engineering

Experiment Design

A/B Testing

Hypothesis Testing

Customer Analytics

Marketing Analytics

Churn Prediction

Demand Forecasting

Production Machine Learning

Tools

Python

SQL

PySpark

Azure

Azure Databricks

Azure Machine Learning

MLflow

GitHub Actions

CI/CD

Graph Databases

Data Pipelines

Git

Version Control

Databricks

PyTorch

Job description

Location & Contract

Location: Hybrid role. 2 days per week in office either: Welwyn Garden City, Hertfordshire or Slough, Berkshire.

Type: Permanent | Full-Time

Key Skills

Senior Data Scientist
Data Scientist
Machine Learning
Artificial Intelligence
Predictive Analytics
Statistical Modelling
Python
SQL
PySpark
Azure
Databricks
MLflow
GitHub Actions
CI/CD
Time Series Forecasting
Propensity Modelling
Feature Engineering
Data Engineering
Azure Machine Learning
Azure Data Platform
Experiment Design
A/B Testing
Hypothesis Testing
Predictive Modelling
Customer Analytics
Marketing Analytics
Customer Lifetime Value
Churn Prediction
Demand Forecasting
Stock Forecasting
Production Machine Learning
Cloud Data Platforms
Data Products
Git
Version Control
MLOps
Data Pipelines

Overview

@mecscomms is recruiting for an experienced Senior Data Scientist to join one of the UK's most advanced Data Science teams, helping shape the future of customer analytics through cutting‑edge machine learning, statistical modelling & cloud‑based AI solutions.

This is a great opportunity for an experienced Data Scientist who enjoys solving complex commercial problems using advanced analytics, statistical modelling & machine learning techniques, whilst delivering production‑ready data science solutions that directly influence customer experience & business performance.

This position offers exposure to large‑scale customer datasets, advanced Azure‑based technologies & highly collaborative multidisciplinary teams consisting of Data Scientists, Data Engineers & Data Analysts.

Purpose

Design, develop & deploy enterprise‑scale data science solutions that improve customer outcomes, enhance commercial performance & support strategic business decision making.

Own the complete analytical lifecycle, from initial problem definition & exploratory analysis through feature engineering, model development, deployment, operational monitoring & continuous optimisation.

Build sophisticated statistical & machine learning models covering areas such as:

Customer Lifetime Value
Customer Propensity Modelling
Customer Churn Prediction
Marketing Optimisation
Personalisation
Time Series Forecasting
Stock Forecasting
Demand Planning
Customer Behaviour Analytics
Decision Intelligence

Technology Stack

Programming

Python
SQL
PySpark

Cloud & Platforms

Microsoft Azure
Azure Databricks
Azure Machine Learning

Machine Learning & AI

Statistical Modelling
Predictive Analytics
Feature Engineering
Propensity Modelling
Time Series Forecasting
MLflow
PyTorch

Data Engineering

Data Pipelines
Graph Databases

DevOps & Development

GitHub
GitHub Actions
CI/CD
Version Control

Core Activity

Deliver end‑to‑end data science solutions from concept to production
Build machine learning models that improve customer & business outcomes
Apply statistical techniques to solve complex business problems
Develop propensity & predictive models for customer decisioning
Design & optimise time series forecasting models
Build scalable data pipelines & production‑ready analytics
Collaborate with cross‑functional teams to deliver business value
Monitor, maintain & continuously improve deployed models
Present analytical insights to technical & business stakeholders
Promote best practice in data science & software engineering

Responsibilities

Take ownership of the complete data science lifecycle, including problem definition, exploratory data analysis, feature engineering, model development, validation, deployment & ongoing optimisation.
Design & execute statistically rigorous analytical approaches, including hypothesis testing, experimental design, uncertainty measurement & business impact assessment.
Develop sophisticated propensity models to support customer targeting, customer engagement, personalisation & commercial decision making.
Build highly accurate time series forecasting models covering both customer demand & stock forecasting, continuously improving forecast performance through back‑testing & model refinement.
Develop scalable, production‑ready machine learning solutions using Python, SQL & PySpark within Azure Databricks.
Build, optimise & maintain robust data pipelines using software engineering best practices, including automated testing, documentation, version control & reproducibility.
Work closely with stakeholders to understand business challenges, define measurable success criteria & translate analytical outputs into commercially valuable recommendations.
Monitor model performance, identify opportunities for optimisation & continuously improve deployed solutions.
Conduct peer reviews of analytical code & statistical models, helping to raise technical standards across the wider Data Science function.
Promote best practice in machine learning, statistical modelling, software engineering & cloud‑based analytics delivery.

Deliverables

Production‑ready machine learning models
Customer propensity models
Time series forecasting solutions
Actionable business insights
Scalable, secure machine learning code
Azure‑based data pipelines
Stakeholder reports & recommendations
Optimised model performance
Well‑documented analytical solutions
Successful cross‑functional delivery

Working Environment

Hybrid Working
Agile Delivery
Azure Cloud Platform
Azure Databricks
Cross‑Functional Product Squads
Enterprise Data Science
Large‑Scale Data Environment
CI/CD & DevOps
Continuous Learning & Innovation

Candidate Profile

Candidates should possess experience as a Senior Data Scientist with strong analytical skills, commercial awareness & a passion for solving complex business problems.

You’ll have a proven track record of delivering end‑to‑end data science solutions, from problem definition through to production deployment, using advanced statistics, machine learning & cloud technologies.

You’ll be confident working with both structured & time series data, building scalable models that deliver measurable business value.

Essential