RMSI

RMSI works with large-scale, real-world data to support advanced analytical and AI systems for global technology clients. The work requires handling raw, imperfect data and converting it into structured, reliable inputs that can be used to train and validate intelligent systems.

I joined RMSI as an Analyst, working on early-stage data annotation and analysis tasks. Within the first month and a half, I requested an internal transition into a Data Analyst role. At the same time, due to an open requirement and demonstrated ownership, I was promoted to Team Lead.

This progression allowed me to move quickly from execution-focused work into a role involving analysis, coordination, quality control, and direct interaction with a confidential global technology client.

My primary responsibility involved working with confidential, sensor-based time-series data generated from controlled activities. The data was accessed through secure cloud systems and processed through multi-stage pipelines before analysis.

I analysed time-series graphs to validate data quality, identify anomalies, evaluate threshold behaviour, and assess whether devices and signals met required standards. The outcomes of this analysis were converted into structured numerical formats suitable for downstream AI training and predictive analysis.

As Team Lead, I owned the central numerical deliverable used by both the team and the client. I designed and maintained an Apple Numbers template that aggregated individual contributions, introduced clear indicators, and reduced ambiguity for reviewers.

• Created and maintained concise instruction summaries on Quip
• Briefed the team on new requirements and clarified edge cases
• Designed and facilitated multi-level quality check workflows
• Acted as the point of contact for two parallel projects
• Regularly communicated challenges and improvement suggestions to the client

Through process optimisation, template design, and clear review mechanisms, both my individual work and my team’s output consistently achieved 98 to 99 percent efficiency and quality scores.

Several structural and workflow suggestions I proposed were adopted by the client, resulting in reduced turnaround time per dataset and clearer interpretation of results.

I also prepared regular performance summaries for managers and stakeholders, covering efficiency, quality metrics, attendance, and overall project health.

I was recognised with the Rising Star Award for my professional contributions and ownership on client-facing projects.

Beyond core work, I actively participated in internal cultural initiatives and won awards in a Rangoli competition and a Best Out of Waste event organised around Environment Day.

This experience shaped how I think about data as a system rather than isolated tasks. It reinforced the importance of clarity, responsibility, and communication when analytical work directly influences how intelligent systems learn and make decisions.