Senior Data Analyst

We are looking for a Senior Data Analyst to drive data-driven decision-making across Selsmart's reverse logistics operations. The role involves analysing large-scale operational and logistics data, building insightful dashboards and KPI frameworks, identifying process improvement opportunities, and providing actionable recommendations to Product, Operations, and Leadership teams. The ideal candidate will have strong expertise in SQL, BI tools, and analytics, along with experience in logistics, supply chain, or operations-focused businesses. This is a high-impact role offering significant ownership and the opportunity to influence business performance at scale.
Responsibilities:
Analyse operational, logistics, and customer data to identify trends, bottlenecks, and improvement opportunities.
Build and maintain dashboards, reports, and KPI frameworks for business and operational teams.
Partner with Product and Operations teams to measure performance and evaluate process improvements.
Develop metrics to track pickup success rates, SLA adherence, partner performance, turnaround times, and operational efficiency.
Perform deep-dive analyses to understand operational issues and recommend corrective actions.
Design and execute experiments to measure the impact of product and process changes.
Automate recurring reports and improve data accessibility across teams.
Work with engineering and product teams to improve data quality, instrumentation, and reporting capabilities.
Present findings and recommendations to stakeholders in a clear and actionable manner.
Requirements:
4+ years of experience in Data Analytics, Business Analytics, or related roles.
2+ years of experience in logistics, reverse logistics, transportation, supply chain, or operations-focused businesses.
Strong SQL skills and experience working with large datasets.
Advanced proficiency in Excel and BI tools such as Power BI, Tableau, Metabase, or similar.
Strong analytical and problem-solving skills with the ability to translate data into business insights.
Experience defining KPIs, building dashboards, and driving operational decision-making.
Ability to work independently and collaborate effectively across teams.
Preferred:
Experience working at Reverse Logistics companies or similar logistics organisations.
Experience analysing pickup operations, transportation networks, last-mile logistics, reverse logistics, or field operations.
Familiarity with Python, statistical analysis, forecasting, or experimentation frameworks.
Experience supporting Product or Operations teams in a high-growth environment.
Key Success Metrics:
Improved visibility into operational performance through actionable dashboards and reporting.
Faster identification and resolution of operational bottlenecks.
Increased pickup success rates and SLA adherence through data-driven recommendations.
Improved partner and field operations performance measurement.
Higher adoption of data-driven decision-making across teams.

Experience
4-8 yrs