At a Glance
- Tasks: Design and deploy data science solutions that enhance customer experiences and drive business performance.
- Company: Join one of the UK's leading Data Science teams at mecscomms.
- Benefits: Enjoy a hybrid work model, competitive salary, and opportunities for continuous learning.
- Other info: Collaborative environment with excellent career growth and exposure to large-scale datasets.
- Why this job: Make a real impact using cutting-edge machine learning and statistical modelling techniques.
- Qualifications: Experience in data science, machine learning, and strong analytical skills required.
The predicted salary is between 63000 - 77000 £ per year.
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
- Py Spark
- Azure
- Databricks
- MLflow
- Git Hub 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
- Py Spark
- Cloud & Platforms
- Microsoft Azure
- Azure Databricks
- Azure Machine Learning
- Machine Learning & AI
- Statistical Modelling
- Predictive Analytics
- Feature Engineering
- Propensity Modelling
- Time Series Forecasting
- MLflow
- Py Torch
- Data Engineering
- Data Pipelines
- Graph Databases
- Dev Ops & Development
- Git Hub
- Git Hub 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 & Py Spark 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 & Dev Ops
- 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
- End‑to‑end data science delivery
- Statistical modelling & hypothesis testing
- Machine learning & predictive analytics
- Propensity modelling
- Time series forecasting
- Python
- SQL
- Py Spark
- Microsoft Azure Cloud
- Azure Databricks
- Feature engineering
- Production ML deployment
- Version control & automated testing
- Data integration & modelling
- Stakeholder management
- Agile delivery experience
Desirable
- MLflow
- Py Torch
- Databricks Asset Bundles
- Graph Databases
- Azure Machine Learning
- Git Hub Actions
- CI/CD
- MLOps
- Marketing Analytics
- Customer Lifetime Value (CLV)
- Churn Prediction
- Retail Analytics
- Customer Personalisation
- Decision Intelligence
- Key Traits
- Curious & analytical
- Commercially minded
- Customer focused
- Strong statistical thinking
- Detail orientated
- Excellent communicator
- Adaptable & delivery focused
For more information or a list of current vacancies, please see our web site at mecscomms. co. uk
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Senior Data Scientist in Slough employer: MECS Communications
Join a dynamic and supportive team as an Assistant Buyer in London, where you'll have the opportunity to thrive in a hybrid work environment that promotes work-life balance. Our company values employee growth and offers comprehensive training and development opportunities, ensuring you can advance your career while enjoying a collaborative culture that prioritises innovation and supplier relationships. With competitive benefits and a focus on procurement best practices, this role is perfect for those looking to make a meaningful impact in the IT and technology sectors.
StudySmarter Expert Advice🤫
We think this is how you could land Senior Data Scientist in Slough
✨Get Involved in Data Science Meetups
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Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like MECS Communications.
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We think you need these skills to ace Senior Data Scientist in Slough
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
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Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at MECS Communications. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at MECS Communications
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at MECS Communications!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
