• Led end-to-end Contact list Segments → to creating Ads audience syncing as a DRI, partnering with LinkedIn, Meta, and Google Ads teams which drove increase of +18% audience creation and an all-time high in LinkedIn audience adoption within one month.

• Collaborated with Google to integrate budget and keyword recommendations for Search and Performance Max Ad Campaigns, surfacing CPC and forecast metrics which led to doubling (2x) increase in first-time ad campaign creation.

• Collaborated with Facebook (Meta) and led the implementation for Facebook CAPI conversion events and data quality improvements as DRI, integrating Dataset Quality APIs to raise baseline event quality (46% → 79%) and scale event syncing from 7,500 to 10,000 events created by users.

• Led Ads frontend modernization and reliability efforts, migrating 100+ components to a new design system and delivering OAuth security hardening, outage handling, reporting integrations, and DFC (Apollo) migrations.

• Adhered to Agile methodologies to thoroughly understand requirements from cross-functional teams; translated complex requirements into actionable tasks, resulting in 100% stakeholder satisfaction

• Devised Python scripts to automate data extraction from multiple IBM databases, enabling seamless generation of comprehensive weekly reports for stakeholders, saving an average of 15 hours per week

• Architected a web application using Flask (Python) for the backend and JavaScript for the frontend, allowing users to generate complex database queries from text-based questions, reducing development time by 60% while increasing accuracy by 85%

• Be the face and voice of the company in his/her campus representing the company’s mission.

• Organize various events like seminars, workshop, hack-a-thons, and how-to sessions.

• Nurture a student community with the knowledge of their technology or services.

• Assisted in a team of 3 on research in the field of Machine Learning/Deep Learning.

• Developed a machine learning model based on Cryptocurrency/Blockchain to predict the price of a particular cryptocurrency on a day using prehistorical data over a period of time and evaluate the model to achieve an accuracy.

• Incorporated scripts using Python to build the model based on Time Series Analysis and perform Feature Engineering Techniques and to fit the data into the model, so that testing can be performed.

• Explored ways to visualize and forecast the evaluated model using Plotly and Matplotlib.

• Deployed the model; with further optimisation produced an impact of accuracy about 90%.

• Contributed on deployment of Machine Learning models.Which includes Prediction, visualisation and Exploratory Data Analysis.

• Worked on Real-time data and implemented Machine Learning algorithms on the data.

• Optimised the performance metrics of implemented models by varying parameters which impacted better accuracy.