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Collaborate with stakeholders to translate business requirements into analyses, analyze large datasets to identify trends, build and maintain dashboards and visualizations (Power BI), validate data quality, support analytics projects (cohort, funnel, retention), and improve reporting processes to drive data-driven decisions.

Collaborate with stakeholders to translate business needs into analytical solutions. Analyze large datasets, build dashboards and visualizations (Power BI), ensure data quality, perform cohort/funnel/retention analyses, monitor KPIs, and support BI reporting improvements to drive product and client lifecycle insights.

Join Tickmill as a Quality Assurance Engineer, focusing on manual and automated testing, improving test coverage, and collaborating with development teams.

Design, develop and support backend services, enhance quality processes with automated tests, collaborate with teams, and maintain code quality.

The Mobile Application Developer (iOS) designs, builds, and maintains high-quality applications for iOS, collaborating with teams to meet functional requirements and solve technical problems.

Collaborate with stakeholders to define requirements, analyze large datasets, produce dashboards and reports (Power BI), perform cohort/funnel/retention analyses, ensure data quality, and support data-driven product and operational improvements.

The Senior AI/ML Engineer will design and deploy AI systems for business impact, lead MLOps practices, and collaborate with teams to drive AI solutions.

Manage and grow assigned retail and professional client portfolios by serving as primary contact, driving platform usage and trading activity, identifying upsell opportunities, performing client reviews, retaining at-risk accounts, coordinating internal teams, maintaining CRM records, and ensuring regulatory-compliant communications.

The Backend Developer (.NET) will design, develop, and support backend services, improve quality through automated tests, and collaborate during integration processes.

Lead design, development, and productionisation of end-to-end ML systems (training, deployment, monitoring, retraining). Drive AI use cases (CLV, churn, fraud), build MLOps/CI-CD pipelines, implement monitoring and explainability (e.g., SHAP), enforce model governance, collaborate with Data Engineering and business stakeholders, and mentor team members to deliver measurable business impact in regulated contexts.