@mecscomms
Senior Data Scientist
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Job Description
Senior Data Scientist
Welwyn Garden City, Hertfordshire or Slough, Berkshire | Hybrid (2 Days Office) | Python | SQL | PySpark | Azure | Databricks | Machine Learning | Statistics | Time Series Forecasting | Propensity Models
Role:
Senior Data Scientist, Lead Data Scientist, ML Scientist, AI Data Scientist, Statistical Data Scientist, Predictive Analytics
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.
Location:
Hybrid role. 2 days per week in office
Either: Welwyn Garden City, Hertfordshire or Slough, Berkshire
Type:
Permanent | Full-Time
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
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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.
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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. Your experience is likely to include some of the following:
Essential:
- End-to-end data science delivery
- Statistical modelling & hypothesis testing
- Machine learning & predictive analytics
- Propensity modelling
- Time series forecasting
- Python
- SQL
- PySpark
- Microsoft Azure Cloud
- Azure Databricks
- Feature engineering
- Production ML deployment
- Version control & automated testing
- Data integration & modelling
- Stakeholder management
- Agile delivery experience
Desirable:
- MLflow
- PyTorch
- Databricks Asset Bundles
- Graph Databases
- Azure Machine Learning
- GitHub 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
@mecscomms:
uniting opportunity with ambition in Telecoms | Media | Technology
@mecscomms is the brand name of MECS Communications Ltd who provide permanent & contract recruitment consultancy service as an Employment Agency & Employment Business. For more information or a list of current vacancies, please see our web site at mecscomms.co.uk
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