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I am Nishita (Nish-ee-ta), a data expert passionate about turning raw data into real impact. From building clean pipelines to uncovering insights with ML, I help teams make smarter, faster decisions.
I'm also diving deep into AI and machine learning—constantly evolving to stay ahead.
I create scalable data systems—so you can focus on what matters and drive meaningful change.
café-hopping for the perfect vibe ☕, deep in a Call of Duty match 🎮, baking something sweet on a whim 🍪, or jotting down poems when the mood strikes ✍️.
You can check out some of my poems here — feel free to dive in!
AI Engineer - Humanitarians AI
Jan. 2025 - April 2025
At Humanitarians AI, I worked with a team to develop AI agentic architectures for healthcare organizations. I focused on building systems that helped healthcare agents manage and interpret medical data, supported better diagnostic outcomes, and improved overall workflow efficiency. I also contributed to the data engineering efforts, helping design reliable data pipelines that ensured clean and accessible information for these AI systems.
Data Engineer - EPM Consultancy
Feb. 2022 – Jun. 2023
I built automated pipelines with AWS and Spark to pull in product and client data into our cloud warehouse. I also set up real-time dashboards and checks to keep everything clean and insights-ready. During my time with the team, I dug into user data and market trends to help shape product strategy and boost feature adoption. I also leveled up Power BI dashboards, sped up SQL workflows, and ran A/B tests to back decisions with real numbers.
Data Engineer - DaVinci Corps
May. 2020 – Aug. 2020
I set up data pipelines in Azure to process daily e-commerce events and feed Power BI dashboards that teams used to track things like stock levels and revenue drops shortly after they happened. I also looked at click and purchase behavior to understand how customers moved through the site and which products were often bought together, and used that work to support a recommendation system. Along the way, I worked with ML engineers to make sure the data going into models was reliable and that model outputs could be used directly in reporting without extra manual steps.