Our client is an industry leading market-research company who use data-driven insights to provide unique and actionable data for some of the world's most recognised brands. Over the last 12 months they've experienced a period of rapid growth and they are positioned to continue this growth for the foreseeable future so it's an ideal time to join the team.
They need a strong Data Scientist to design, develop and deploy systems to monitor data health.
Key Responsibilities:
•Design and implement monitoring and alerting systems to ensure the reliability and accuracy of key datasets and processes.
•Collaborate with teams to define relevant metrics, thresholds, and KPIs.
•Build, maintain, and productionise machine learning and statistical models using Python and PySpark.
•Design and implement automation tools which can help dynamically adapt our products to external changes
•Integrate LLM tooling into pipelines to aid with automation
•Deploy monitoring tools and models using AWS infrastructure.
•Investigate and troubleshoot anomalies in the data pipeline.
•Promote data quality and monitoring best practices across the business.
•Contribute to a culture of curiosity, rigour, and innovation.
•Apply automation and AI-assisted tools where appropriate to improve delivery efficiency and the quality of analytical outputs.
Skills Needed:
•Proficiency in Python and SQL for analysis, model development, and data interrogation.
•Comfortable deploying statistical or ML models into production environments.
•Strong understanding of cloud infrastructure, preferably AWS.
•A methodical, problem-solving mindset with high attention to detail.
•Able to scope, define, and deliver complex solutions independently.
•Comfortable working closely with non-technical stakeholders to define business-critical metrics.
•Self-motivated, accountable, and keen to continuously learn and grow.
•Previous experience building monitoring or data quality frameworks is highly desirable.