Data Scientist

Data Scientist Location: Fully Remote (UK) Contract Length: Initial 6 Months (Extension Possible) Rate: Up to £300 per day We are seeking a highly motivated Data Scientist to support the development of an innovative, data-driven recommendation capability. This solution will leverage multiple enterprise data sources to identify and predict the most likely owning application for orphaned DNS records. You will work with large, complex datasets including DNS, CMDB, infrastructure, ownership, and ServiceNow data, applying machine learning techniques to create intelligent recommendation models that improve data quality, governance, and operational efficiency. This is an excellent opportunity for a Data Scientist who enjoys solving complex business problems through data, analytics, and machine learning within a cloud-based environment. Key Responsibilities

• Develop machine learning models to identify and predict application ownership of orphaned DNS records.
• Analyse and correlate data from multiple sources including:
• DNS records
• CMDB platforms
• ServiceNow
• Infrastructure and ownership datasets
• Design, build, and optimise recommendation algorithms.
• Perform feature engineering and data enrichment activities to improve model accuracy.
• Extract, transform and analyse large datasets using SQL and Python.
• Deploy and maintain machine learning solutions within cloud environments.
• Work closely with infrastructure, cloud, and service management teams to understand data relationships and business requirements.
• Evaluate model performance and continuously improve predictive capabilities.
• Document methodologies, assumptions, and recommendations for technical and non-technical stakeholders. Required Skills & Experience
• Proven commercial experience as a Data Scientist.
• Strong SQL skills with experience querying and analysing large datasets.
• Advanced Python development skills.
• Hands-on experience with:
• Scikit-learn
• Pandas
• NumPy
• Machine Learning model development
• Experience building and deploying predictive analytics or recommendation models.
• Strong understanding of supervised and unsupervised machine learning techniques.
• Experience with data cleansing, feature engineering, and model optimisation.
• Ability to communicate complex findings to technical and business stakeholders. Desirable Experience
• Experience working with DNS, CMDB, ServiceNow, or IT asset management data.
• Knowledge of infrastructure and cloud environments.
• Experience with:
• AWS
• Azure
• Amazon SageMaker
• Azure Machine Learning Studio
• Understanding of MLOps and model deployment best practices.
• Experience within large enterprise or transformation environments. If you are interested please apply or send your CV to luke.sandilands@cpl.com