FIND_THE_RIGHTJOB.
Senior Data Scientist
JOB_REQUIREMENTS
Hires in
Not specified
Employment Type
Not specified
Company Location
Not specified
Salary
Not specified
Senior Data Scientist is responsible for optimising product performance and overall business performance by taking and owning data-driven solutions. This includes analysing product and learner data, identifying gaps in system and user flows, and translating these into practical data science initiatives - analysis, modelling, automations, and targeted product enhancements. The role requires strong ownership and problem-solving capability to drive meaningful impact.
Roles and Responsibilities
- Execute end-to-end data tasks with high ownership, from scoping and analysis to implementation and evaluation, ensuring timely delivery and consistent alignment with organisational goals.
- Analyse product and learner data to uncover performance patterns, identify gaps in current flows, and highlight areas where data-driven improvements can deliver measurable value across product, academic, and operational functions.
- Plan structured, hypothesis-led analyses with clear objectives, scope, methods, and required data, ensuring comprehensive coverage of the problem, minimising bias, and enabling reliable findings that directly support the intended decision or solution.
- Build analytical models, automations, and targeted enhancements that improve product performance, streamline internal processes, and support delivery of personalised and efficient learner experiences.
- Monitor and evaluate the performance of implemented solutions, identify opportunities for continuous improvement, and iterate on models or automations to sustain and enhance long-term impact.
- Develop and deploy monitoring and alerting systems to continuously track the performance and running state of existing models, automations, and reports, ensuring timely detection of issues and maintaining reliability across all deployed solutions.
- Take full ownership of the accuracy, reliability, and completeness of all data outputs-including reports, analyses, automations, and model results-ensuring every deliverable presented to stakeholders is error-free, decision-ready, and aligned with rigorous data integrity standards.
- Conduct technical research to explore, test, and apply advanced modelling approaches-such as improved predictive techniques, clustering strategies, feature engineering methods, and algorithmic enhancements-to strengthen the robustness, accuracy, and long-term effectiveness of deployed data science projects.
- Develop clear insights, reports, and dashboards that translate complex findings into simple, actionable recommendations for leadership and cross-functional stakeholders.
- Deploy models and automations to the server environment, integrate them into existing workflows, and manage the operational aspects including versioning, monitoring, troubleshooting, and collaborating with DevOps/engineering teams to ensure reliable production performance.
- Create and maintain clear, comprehensive documentation for analyses, models, automations, pipelines, and deployed solutions to ensure transparency, reproducibility, and smooth handover across teams.
- Collaborate with product, academic, and operational teams to understand context, refine requirements, and deliver outputs that are actionable, accurate, and aligned with real functional needs.
- Proactively develop a strong understanding of the product, services, user journeys, and operational workflows to ensure data science initiatives are context-aware and consistently contribute meaningful value across functional areas.
- Pursue continuous learning and upskilling in data science tools, methods, and domain understanding to remain updated with emerging practices and improve the quality and relevance of work.
© 2026 Qureos. All rights reserved.