Senior Data Scientist – Consumer Twins

Jobtailor; Reading; On-site; GBP 60,000 - 100,000; Full time

Job summary

Jobtailor in Reading, United Kingdom, is seeking an experienced Data Scientist to develop and deploy production ML models. You will own the full data science lifecycle from problem framing to monitoring and optimization.

You will work with engineers and business teams to translate hypotheses into scalable predictions, handling large datasets and delivering measurable value in a fast-paced environment.

Qualifications

• Solid experience as a data scientist on real-world, production problems.
• Strong Python skills or an equivalent language used in applied data science.
• Experience with supervised and unsupervised techniques such as anomaly detection.
• Exposure to cloud-based model training, deployment and automation.
• Strong ownership and accountability for data science outputs.
• Experience with large consumer datasets or high-traffic platforms is advantageous but not essential.

Responsibilities

• Develop and deploy machine-learning models in production environments.
• Work end-to-end across the data science lifecycle: problem definition, modelling, deployment and monitoring.
• Analyse new and existing data sources to improve decision-making and model performance.
• Design feedback loops that continuously improve outcomes and data quality.
• Collaborate closely with engineers to deliver scalable, reliable model predictions.
• Communicate technical insights clearly to non-technical stakeholders.
• Partner with engineering, operational and commercial teams to take ideas from hypothesis through deployment.
• Ensure models deliver real value once live.

Skills

Python Programming

Data Science Lifecycle

Anomaly Detection

Cloud Deployment

Ownership & Accountability

Production ML Experience

Tools

Cloud Platforms

Version Control

Data Processing Frameworks

Model Automation Tools

Job description

• Develop and deploy machine-learning models in production environments
• Work end-to-end across the data science lifecycle: problem definition, modelling, deployment and monitoring
• Analyse new and existing data sources to improve decision-making and model performance
• Design feedback loops that continuously improve outcomes and data quality
• Collaborate closely with engineers to deliver scalable, reliable model predictions
• Communicate technical insights clearly to non-technical stakeholders
• Partner with engineering, operational and commercial teams to take ideas from hypothesis through deployment
• Ensure models deliver real value once live

Requirements

• Solid experience working as a data scientist on real-world, production problems
• Strong Python skills or a similar language used in applied data science
• Experience with supervised learning and unsupervised techniques such as anomaly detection
• Exposure to cloud-based model training, deployment and automation
• Strong ownership and accountability for data science outputs
• Experience with large consumer datasets or high-traffic platforms is advantageous but not essential
• Right to work in the United Kingdom; visa sponsorship and relocation support are not available

Core Competencies

Demonstrates expertise in developing and deploying machine-learning models, with strong proficiency in Python and experience across the data science lifecycle. Capable of collaborating with cross-functional teams to ensure models deliver real value and improve decision-making.

Highest-signal resume keywords

• Machine-Learning Model Development
• Python Programming
• Supervised Learning Techniques
• Cloud-Based Model Deployment
• Data Science Lifecycle Management

ATS Optimization Keywords

Hard Skills

• Machine-Learning
• Data Analysis
• Model Deployment
• Anomaly Detection
• Data Quality Improvement
• Model Monitoring
• Data Science
• Statistical Analysis
• Predictive Modeling
• Data Visualization

Soft Skills

• Collaboration
• Communication
• Ownership
• Accountability
• Problem-Solving

Industry Keywords

• Data Science
• Production Environment
• Consumer Datasets
• High-Traffic Platforms
• Feedback Loops

Tools & Technologies

• Cloud Platforms
• Data Science Tools
• Version Control Systems
• Data Processing Frameworks
• Model Automation Tools