- Managed and trained a team of AV Technicians, overseeing shift scheduling, equipment handling, and documentation processes using tools such as Microsoft Excel and Mazevo for efficient planning and operations.- Operated, maintained, and set up AV rental equipment for events, ensuring seamless technical support and high-quality service with Crestron, Shure audio solutions, QSC systems, and Blackmagic Design equipment.- Collaborated with event planning teams to deliver AV solutions tailored to client needs, leveraging platforms like MS Teams, Zoom, and 7pointops for communication, coordination, and scheduling.- Ensured the safe handling and maintenance of high-value AV equipment, implementing regular quality checks to uphold standards while utilizing Lenovo systems running Windows for diagnostics and control.- Delivered professional client-focused technical support, addressing issues promptly while integrating AV systems with tools such as PowerPoint for presentations and live event streaming.- Conducted team training sessions and evaluations to enhance technical proficiency, ensuring staff competence in operating industry-standard AV equipment and software solutions.

- Transformed a legacy script from R to Python, enhancing support for evaluating various DMA tests and control sets for experimentation.- Conducted a DMA time-based regression test to estimate the incremental return on ad spend (iROAS) while preserving user privacy.- Implemented parallelization, optimizing test and control set selection, resulting in a 4% reduction in required lift and a 32x speedup.- Applied distance-based metrics to select DMAs based on spillage, leveraging Folium, GeoPandas, and Google MyMaps to provide geographic insights.- Developed non-linear regression models to identify prediction intervals, significantly reducing the Mean Squared Error and the minimum required lift for achieving statistical significance.- PuLP and constrained optimization (integer and elastic constraints) were used to select DMAs based on region, brand presence, and business KPIs for the test set.- Built and vetted data pipelines for Marketing Mix Models, collaborating with business partners to organize and collect data from multiple channels.

- Developed a feature on the app that displays the next available timestamp of a product based on its status in the supply pipeline. Constructed the end-to-end business logic using the microservice architectural pattern. Furthermore, developed the supply system end-to-end using the Django framework for APIs and data models, Celery for crons, and Kubernetes and docker for deployments.- Leveraged information received from the 'next available timestamp' project to indent the availability of in-demand products.- Developing a notification system and an analytics dashboard that’ll generate insights to gauge the performance of notifications and help understand factors that should be considered while sending them- Deployed a quantity-based recommendation engine to conditionally increase a customer's purchase size and hence their average order value.

- Conducted a user research study to understand how we can improve cart penetration and overall sales of Fruits and Vegetables on our platform.- Conceived a prototype for a system that would liquidate excess FnV stock during the non-critical purchase period at a discounted rate. The primary metric here was to reduce dump (end-of-day), improve cart penetration of FNV during low-purchase periods, and improve average order value.- Experimented with a model where stores would be allowed to open 24x7 as a means to improve Night Active Users (NAU) and total daily orders - created a dashboard to display store progress using metrics such as conversion. Furthermore, investigated whether NAU is a function of organic growth and identified critical assortment.- Developed an alternative portable store expansion strategy that allowed us to deploy stores in locations not accessible by conventional stores in 10 minutes. This strategy also helped us employ a new 5-minute delivery strategy. The success metrics were conversion and new users acquired. Assortment optimization was performed and deemed vital due to space constraints in this scenario.- Deployed a new experiment to assimilate the perception of fresh produce on the platform. Initial iterations were executed with fresh paneer as a proxy for freshness. Identified areas to service via hexagonal clusters based on order density, accessibility, and low sales of fresh assortment; identifying and monitoring the growth of metrics such as cart penetration/ Furthermore, also tried to gauge whether sales are limited by availability.- Designed a framework to generate an assortment that would nudge users in tier two cities to transition to the 10-minute model. The framework was developed and benchmarked in accordance with data of tier-one cities (under the assumption that customers understand the value proposition). Users' orders were selected using an RFM analysis.

- Optimized and experimented with variations of reorder = a component of the app that suggests items previously purchased by the customer. Created a flask-based API that serves product variations based on sorting, categorization, and item weightage. Furthermore, employed an A/B test and monitored metrics (such as an increase in Average Order Value) in order to draw insights for further iterations.- Monitored an experiment that deals with aiding new customers on the platform, while also identifying novel pain points that might appear. The primary metric of interest was improving new user conversion, while risk metrics included drop-offs at specific points in the app. Furthermore, conducted an in-depth data exploration into why customers selected the call option but didn't call us.- Prototyped a nutritional section on the app for a hackathon in order to improve cart penetration of 'healthy' items via a new use case. Furthermore, identified an assortment that would be optimal for this page.- Re-introduced a JIT supply chain used for COVID disruption mitigation. Optimized a system used to transfer stocks from warehouse to intermediate location; creating a system to forecast demand of fresh produce and raising indent; created a holistic dashboard that gives an overview of the end to end operations such as order fulfillment rate; conversion, stock transfer fulfillment rate, and stock availability. Furthermore, created a script that generates a daily report and flags anomalies in metrics.

- Parallelized implementation of Capacitated Vehicle Routing Problem (CVRP) using Google’s ORtools and multiprocessing. Enabling multi-cores allowed for a 4-fold speedup in inference time.- Built an end-to-end dashboard to identify time-consuming bottlenecks of the supply chain. The dashboard led to the inception of experimentation with vehicle utilization in the first mile, thereby clearing up the dispatch area and improving order throughput. - Devised an alternative supply chain (based on a Just In Time model) to cope with the disruptions induced via COVID-19. The JIT model cut down on lean wastes such as picking, billing, and packaging while allowing for flexible capacity to tackle disruptions. Created an ETL-based software system that transfers stocks from a warehouse to an intermediate warehouse; responded to ad-hoc software requests; created and monitored metrics to track the success rate of stock transfer operations

- Devised dynamic agroclimatic zones for the Indian peninsula using K-means, curtailing human intervention. Best k was chosen using Silhouette, Calinksi, and Davies Bouldin metrics for good cluster homogeneity per historical climatic data.- Established and supervised an Innovation Hub for developing rapid solutions via design sprints. Sprints allowed the team to prototype new ideas quickly, eliminating building a scalable system.- Engineered a crop recommendation model to provide actionable insights and growth parameters to enhance yield via a Greedy Approach. This, coupled with agroclimatic zones, would enable farmers to minimize crop failures during harsh conditions.

- Detected anomalies in product dimensions and weight to initiate weight-based payout, reduce packaging waste, optimize warehouse storage and automate error checks.- Determined an optimal assortment series to be held at an intermediate location to reduce customer promise time – utilized a hybrid Frequent Pattern Growth Algorithm alongside a Greedy Approximate Algorithm for a set cover problem.- Part of the Innovation Hub – a team dedicated to optimizing parts of the supply chain with the objective of reducing cost per order.- Devised a new warehouse picking strategy in order to cut down the processing time of the order and increase a picker’s efficiency.- Data simulation was initially used to evaluate its effectiveness, followed by hypothesis testing with data from a controlled experiment.- Formulated a new warehouse placement strategy based on historical data to prune picking time. Also, recognized sub-optimal parts of the picking process.- Identified parts of the last mile pipeline that were bottlenecked – such as packaging time for the delivery partner. Clustered orders based on distance – enabling us to pre-pack orders.- Employed DBScan and Folium (a Geospatial library) to identify and visualize closely packed dense order clusters to optimize delivery routes and payouts for delivery partners.

Prepared scripts, created segment sheets and running orders.Hosted live shows and live bytes.

Supervised and delegated all the club duties to enable members to perform their roles effectively. Prepared an extensive framework and material to recruit new members for the English Department.

Researched and developed promotional material and show description, ensuring that all marketing materials maintained a high level of quality and positively promoted the brand.

- Conducted statistical analyses using Excel to leverage the results and drive decision making.- Optimized factors for sales conversions and designed an LSTM for deal recommendations.- Dynamically integrated product predictions onto a web page using a Django Framework.Performed image Recognition and Classification of food items using CNNs.

- Audited stores for consumer experiences and defined areas with potential to improve.- Provided an update on contact lens market size and consumer dynamics and ensured survey programming was correctly implemented}- Designed a data dashboard to dynamically capture and visually track the condition of the market and Acuvue’s range of contact lenses.- Utilized data-driven tools to deliver actionable insights for consumer price preferences.

- Responsible for achieving organic growth among clients using social media as a platform.- Created a consolidated list of Brands to help coordinate cross-divisional opportunities.- Designed marketing material to acquire new firms and connected them with Content Creators.

Non-Profit Youtube Production for School.