Strategic Impact:

Leading analytics operations for Lending, Insurance, and Investments. Managing a team of 7 Product Analysts to oversee funnel performance, automated reporting, and marketing analytics.

• Snowflake & SQL
• Tableau
• Google Analytics, Firebase & MixPanel
• Excel & Python
• Stakeholder Management

Strategic Impact:

Directed comprehensive data strategy and client reporting. Managed analytical teams, built robust automated dashboards, and established event tracking architectures for mobile and web to optimize structural performance.

• Google Analytics, Firebase & Tag Manager
• Neo4j & SQL
• Microsoft Azure, Databricks & Pipelines
• Excel & PowerBI
• Stakeholder Management

Project Highlights :

1. Spearheaded the strategic migration from Adobe Analytics to Google Analytics across all mobile applications. This initiative delivered significant cost savings while fostering a unified data ecosystem across web and mobile platforms. By leveraging Google Analytics, we achieved enhanced data consistency, streamlined reporting, and improved cross-platform insights, empowering data-driven decision-making and optimizing user experiences.

2. Revamped the analytics infrastructure by architecting a robust data foundation with Google Tag Manager and Google Analytics. This involved establishing a secure data warehouse for historical GA data and constructing efficient data pipelines. This strategic overhaul significantly enhanced data quality and accelerated insights generation. By streamlining data collection and processing, we empowered data-driven decision-making across the organization, leading to improved operational efficiency and informed business strategies.

3. I identified a recurring user pain point: frequent manual searches for the same content within the search bar. To address this, I spearheaded the development and implementation of a "Follow User" feature. This innovative solution leverages user interaction history to proactively surface relevant content, eliminating the need for repetitive manual searches. Post-implementation analysis revealed a significant reduction in search query volume, demonstrating a marked improvement in user efficiency and a streamlined user experience. Furthermore, the "Follow User" feature has driven a notable increase in user engagement with recommended content, suggesting a deeper level of content consumption and a more personalized user journey. This data-driven approach not only enhances user satisfaction but also optimizes resource allocation by minimizing redundant search efforts.

Strategic Impact:

Led Top-of-Funnel operations, SEO analytics, and the OneCard Referral Program. Spearheaded cross-functional data initiatives, leading analysts through complex marketing optimization and product scaling experiments.

• Google Analytics, Firebase & AppsFlyer
• Athena, Bigquery & AWS
• Python, Sagemaker & Superset
• SQL and Excel
• Stakeholder Management

Project Highlights :

1. I spearheaded an initiative to optimize the top-of-funnel performance of One Card. This involved establishing a robust data monitoring and analysis framework. I automated data flow to a Superset dashboard, enabling real-time insights KPIs. By leveraging these insights, I optimized Google and Facebook ad campaigns, refining target audience segments and campaign objectives. This data-driven approach resulted in a significant uplift in key top-of-funnel metrics, including [mention specific metrics like click-through rates (CTR), cost-per-click (CPC), or conversion rates]. These improvements directly translated into increased brand awareness, higher customer acquisition rates, and ultimately, improved return on ad spend (ROAS).

2. I conducted a comprehensive analysis of referral campaign performance, leveraging available datasets to identify key areas for optimization. Based on these insights, I implemented a user-specific targeting strategy, tailoring referral incentives and messaging to individual user segments. This data-driven approach resulted in a significant uplift in referral conversion rates, surpassing pre-defined targets. Furthermore, this refined targeting strategy led to a more efficient allocation of marketing resources, reducing customer acquisition costs while simultaneously increasing the overall quality of referrals. By demonstrating the power of data-driven decision-making, this project not only improved the effectiveness of referral campaigns but also established a foundation for ongoing campaign optimization and continuous improvement.

Strategic Impact:

Advanced to Analytics Lead after successfully restructuring analytical operations. Managed a team of 11+ analysts, scaling event tracking infrastructure and developing automated data pipelines to deeply optimize the core product.

• Google Analytics, Firebase and Tag Manager
• MongoDB, BigQuery and Redash
• Python, Airflow and Google Apps Script
• SQL and Excel
• Stakeholder Management

Project Highlights :

1. Optimize the leads being ingested into the CRM, save man hours and improve conversion rate. This problem was solved by analysing the data for that particular lead source right from the user acquisition source to user activities, thier time taken and so on. New lead source was created with updated criteria and all the metrics improved by 2-5x. This was done using mongoDB, python and Redash.

2. The project that had impact both on business and product level was about finding the Insights related to the user's first core (tests, quiz, video etc) activity. The first core activity of the user for any product generally determines whether the user will be engaging on the platform futher or will drop off without any engagement/getting monetised. The user's that gets monetized are the one's that does some engagement on the platform and we had a similar assumption. But, after we did the analysis for the same the results were kinda surprising, to our surprise around 30% of users for that particular date range did no activity but got monetized. To dig this deep futher we checked their signup and acquisition source, platform age and other non core activites, activites after getting monetised etc and, yes we did find the pattern for a set of users. We then proceeded to get the users from similar sources and made others do similar non core activites by making the app journey adjusted for them and we are able to see considerable increase in the engagement (10%) and the revenue(3.5%). This entire task had a lot of data stitching and analysing work, from getting data from Analytical Tools to Getting data from the database to automating it for the ease of access of data to stitching Analytical tool data with DB data. The tech and tools used were python, SQL, MongoDB and Webengage

3. Making android users journeys better for selling more courses on the platform. We added a new sub-product that offered free videos to the user and then the relevant course was pitched to the user on the video page. The earlier flow was broken since the user had to make 10 clicks to reach the pitched course but after analysing the user data on the conversions, click rates etc we added a direct for the user to reach the course via free video while keeping the video running in pip mode to not let the experience fade away. We indeed saw better conversions rates for the courses and more traffic on the courses. This was done using mongoDB, SQL and python.

What I did here?

I joined Zebi as a Software Engineer about a year and half ago. This was my first full time job. I worked on various technologies here. Name it, I've worked on it. Listing a few below.

• Data Analytics & Visualization - Python & D3.js
• Blockchain
• Web Development & SQL
• Content Writing

Among the things mentioned above, the tech that I've worked on the most is Data Analytics & Visualization

Project Highlights :

1. Hotel management software data analysis to find the hotel hoppers, user behaviour, high traffic areas and seasonality in the hotel data. It was done using python and tableau.

2. Zebi Crypto Platform's day to day analysis on traffic, volume, price, nodes, blocks created and so on.

What I did here?

I joined Systech Solution as Data Analyst - Intern. This was probably the best learning experience ever. This started from new year'18 and went all along until April. The learning schema here has helped me alot to grasp the tech mentioned down below.

• SQL
• Tableau
• Oracle SQL & Developer

Oh, This was my last internship BTW. After which I joined Zebi.

What I did here?

I joined Vtech as an Analyst - Intern. I worked here mostly on analysing their client's data and making Visualizations. Also, learned some stuff about the branding world. The tech I've worked here are:

• Excel
• R Programming
• PowerBI

This was that time when I fell in love with R. Although, I haven't worked more on it during my fulltime but just a quick refreshment would do the work. Check my GITHUB for R projects.

What I did here?

At this moment I got exposed to the Corporate world. The first impression was quite good. I developed the interest in Data Science just here. Got to work on Analytics projects and learning curve was good. The tech I've used here are:

• Digital Marketing
• Excel and javascript
• Tableau

From forecasting to time series analysis, I have done it all here.