Work and experience

1 - Network Operation Associate (Analytics & Reporting Focus) — UST (formerly UST Global) | Gurgaon, India

September 2022 – August 2024

My role at UST was a dual-function position on Nokia's enterprise account, combining frontline B2B operational support with hands-on data analysis and reporting. Over time, the analytical side grew into the larger half of the role, as I took ownership of how the team measured and improved its own performance.

I wrote complex SQL queries against multiple internal systems to extract employee activity and customer interaction data, running trend and root-cause analysis on operational anomalies — including call drop patterns and ticket escalation drivers. Acting on these insights helped reduce unresolved tickets by 15% and lift SLA compliance by 10%. I designed and maintained Power BI and Excel dashboards covering KPIs for 50+ employees across Nokia's Gurugram outlets, presenting findings directly to managers and contributing to a 12% productivity improvement.

To eliminate recurring manual effort in reporting cycles, I built Python data pipelines that automated the end-to-end reporting workflow. This cut manual effort by 60%, saved 6–7 hours of work per week, and lifted reporting accuracy to 98% by managing data lineage and validation properly. I also documented data definitions, assumptions, and reporting logic so outputs could be reused and trusted across the team — turning one-off analyses into reusable team capability.

Alongside this analytical work, I held a concurrent core responsibility for B2B technical support — managing 100+ customer and partner queries per week through Salesforce CRM and Cloud Genesis, sustaining customer satisfaction above 90% and meeting SLA timelines throughout.

Key Outcomes (Impact)

  • Reduced unresolved tickets by 15% through SQL-based root-cause analysis on operational anomalies.
  • Lifted team productivity by 12% via Power BI and Excel dashboards covering 50+ employees.
  • Improved SLA compliance by 10% through data-backed performance tracking and stakeholder reporting.
  • Cut manual reporting effort by 60% (saving 6–7 hours per week) and lifted accuracy to 98% via Python automation.
  • Sustained customer satisfaction above 90% across 100+ B2B queries per week as a concurrent responsibility.

What I Learned

This role taught me how to frame business problems as data questions in a real production environment, the value of data storytelling for non-technical stakeholders, and how good documentation turns one-off analysis into reusable team capability. It also showed me how lightweight automation compounds over time — the scripts I wrote continued saving hours every week long after the initial build.

2. Junior Data Analyst — Professional World Leather | Noida, India

October 2019 – September 2022

My first analytical role, working with the commercial team of a B2C retail business across multiple stores. I owned pricing and margin analysis on 1,200+ products, applying structured investigation and assumption-testing across product segments to improve gross margin by 3.5% and reduce stockouts by 15%. The work taught me that every analytical question, however technical, has to tie back to a commercial decision someone needs to make.

Reliable analysis depended on reliable data, and the inputs rarely cooperated. I cleaned, validated, and reconciled large sales and inventory datasets coming in from multiple store systems — using Pivot Tables, Power Query, XLOOKUP, INDEX-MATCH, and VBA — and lifted overall data accuracy above 98% across the reporting pipeline. Where source systems disagreed, I tracked the discrepancies back to the upstream issue rather than silently patching them downstream, which became a habit I've kept since.

On top of cleaned data, I built dynamic Excel dashboards and monthly MIS reports tracking sales, returns, inventory levels, and store and employee performance — giving managers a near real-time view that supported day-to-day commercial decisions on stocking and pricing. I also automated recurring weekly reports using Excel formulas and VBA, saving roughly 6 hours per week and reducing reporting errors by 70%, which freed up time for deeper analysis rather than manual preparation.

Alongside structured reporting, I supported store and finance teams with ad-hoc analysis and data validation requests — investigating discrepancies, answering one-off questions, and translating raw data into clear answers for non-technical stakeholders.

Key Outcomes (Impact)

  • Improved gross margin by 3.5% through pricing and margin analysis on 1,200+ products in a B2C retail environment.
  • Reduced stockouts by 15% by surfacing inventory and demand patterns through structured analysis.
  • Lifted data accuracy above 98% across the reporting pipeline via cleaning, validation, and multi-source reconciliation.
  • Built Excel dashboards and monthly MIS reports tracking sales, returns, inventory, and employee KPIs in near real time.
  • Automated weekly reporting workflows, saving ~6 hours per week and reducing reporting errors by 70%.

What I Learned

This role gave me a commercial mindset — every piece of analysis I worked on tied back to a real business decision someone needed to make, which kept the work focused and useful. It also built the habit of treating data quality as foundational rather than optional: clean, well-documented data is what makes everything downstream — dashboards, decisions, automation — actually trustworthy.