2016 to 2017 · India

Cognizant Technology Solutions.

Programming Analyst · Data Science & Analytics

Core responsibility

Deliver data analysis, anomaly detection and Power BI reporting for a US life sciences client, and automate the manual reporting work that was slowing every delivery cycle.

AnalyticsAutomationPower BIClient Delivery
  • This was my first corporate role, taken after my first venture closed, to build a solid technical base. It's also where my interest in data started: the first time I saw numbers change a business decision.
  • I worked in Data Science and Analytics for a US life sciences client, handling data analysis, anomaly detection, backups, and Power BI reporting.
  • I automated their reporting from end to end, cutting development time by about 40% while improving client satisfaction.
  • It taught me real production discipline, and showed me how much time companies lose to repetitive manual work. I left with two things I kept building on: a habit of asking what the data says, and a dislike of manual processes.

Major projects

01

Automating Client Reporting Operations

Reporting AutomationPower BIClient Delivery
Challenge

Reporting for the client was built and refreshed manually, consuming development hours on repetitive work and slowing every delivery cycle.

What I did

Designed and implemented an end-to-end automation of the reporting operations, from data preparation through to Power BI dashboards the client actually used. Development time dropped by around 40%, and the freed-up hours went into work the client valued more.

02

Anomaly Detection & Data Reliability

Anomaly DetectionData QualityProduction Discipline
Challenge

In life sciences, the data behind a report matters as much as the report itself. Errors that slip through are expensive to unwind once a client has acted on them.

What I did

Built anomaly detection into the data workflow to catch irregularities before they reached the Power BI reports, defined business rules for what clean, valid data should look like, and set up reliable backup routines so nothing depended on a single copy of anything. It was my first real lesson in production discipline: quality is something you build into the pipeline, not check at the end.

Skills applied

Data QualityAnomaly DetectionSQL & Data PipelinesStakeholder Communication

Key learnings

Big IT services taught me discipline: production data, real clients, and deadlines that don't move. It also taught me that quality is built into a pipeline, not checked at the end, and that repetitive manual work is where enterprises quietly lose the most time. I have been building against both ever since. It was also the start of a thread that runs through every role since: data first, decisions second.

What this role proves

That the technical foundation under my product work is real, built in production for a paying client, and that my data journey began here, not in a classroom.

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