Hrishi Sahu

Parul Institute of Pharmacy and Research

M.Pharm · Pharmaceutical Analysis

August 1, 2023 – June 30, 2025

Shri Ram Institute of Pharmacy

B.Pharm

August 1, 2017 – June 30, 2021

Advanced Medtech Solutions Pvt ltd,Vadodara(Gujarat)

Quality Department

January 1, 2025 – April 1, 2025

Vadodara, Gujarat, India

Daymark Pharmaceuticals Jabalpur(M.P)

Industrial Trainee

March 1, 2021 – April 1, 2021

Jabalpur, Madhya Pradesh, India

Case Study regarding Analyzing Bumble Profiles

June 19, 2026 – Present

Engineered a data cleaning and exploratory pipeline for a robust retail/lifestyle dataset containing 59,946 user profiles across 17 unique categorical and numerical dimensions (including metrics such as age, location, income, lifestyle choices, and profiles' completion rate). Uncovered geographic hubs finding that greater than 99.8% of user traffic skew explicitly in California, peaking distinctively inside student/young professional micro-clusters like Berkeley and Palo Alto where over 60% of the demographic is under 30 (correlating heavily with UC Berkeley & Stanford regional university lifecycles). Calculated income polarization revealing that while over 75% of users capture entry-level or student bands (less than $100K), a dense outlier demographic group accounting for roughly 5% explicitly accounts for ultra-high income brackets (greater than $500k). Isolated behavior matrices via normalized cross-tabulations revealing severe habit-based traits, demonstrating that Vegans maintain a 4x higher probability to be strict non-drinkers (18.1%) compared to standard diets (4.8%). Premium Tier Tiering: Formulated strategy to introduce high-tier, specialized matchmaking algorithms focused on the 5% ultra-high-income outlier demographic profile tier. Filtered In-App Match Filters: Recommended deploying explicit dietary/lifestyle tag match parameters within the core matching UI to tap into strict user segment preferences (e.g., highly strict Vegan habits).

Data Analysis the Pharmacy Over The Counter (OTC) Sales Drug Analysis.

June 19, 2026 – Present

Cleaned and analyzed comprehensive operational datasets tracking OTC medical shipments across 5 global regions (USA, UK, Canada, India, and Australia), aggregating ≈ $59K in revenue and 3,488 units shipped. Product Dynamics: Isolated Digestive Enzymes as the core revenue engine ($11,056; 612 units shipped), whereas Pain Relief Tablets underperformed as the lowest volume contributor ($5,993; 354 units). Geographic Momentum: The USA and UK drove the highest market demand (795 and 752 boxes shipped), while Australia represented the smallest regional footprint (587 boxes). Temporal Trends: Pinpointed heavy purchase velocity surges on Fridays ($10,651) and Wednesdays ($9,367), with key monthly sales spikes concentrating in May and July. Salesforce Reliance: Discovered a high organizational reliance on top talent, with just two sales representatives (Rajesh Patel and Nikhil Batra) accounting for over 36 percent of total global sales revenue. Streamline Inventory Channels: Align global supply chains to increase stock allocation of high-demand medications to the USA and UK hubs ahead of the identified May/July volume surges. Capture Week-End Momentum: Implement targeted promotional incentives or distributor ordering windows on Wednesdays and Thursdays to fully capitalize on Friday checkout waves. Standardize Sales Playbooks: Audit the account management strategies of the top two revenue-driving representatives to train and upskill lower-grossing sales team members.

Graduation Project for Chocolate Company Retail Analysis for improving profit efficiency. (Portfolio)

June 19, 2026 – Present

Processed 1,000,000+ (1 Million) transactional sales records integrated across 50,000 unique customer profiles, 200 product SKUs, and 100 retail outlets. Evaluated international operational data for 5 premium mass-market confectionery brands across 6 major global markets (USA, UK, Canada, France, Germany, and Australia). Uncovered a recurring seasonal "February Slump" where net profit plummeted to its annual floor (≈ $970K) across consecutive fiscal years due to post-holiday consumer fatigue. Identified distinct "life-stage purchasing drops" at specific demographic brackets (ages 25, 38, and 52), contrasted by high-value spending peaks clustering around ages 20, 32, and 46. Isolated stark performance distributions by retail channel: Paris led global Airport-based revenue (> $2M), while Sydney overwhelmingly captured the Online sector (≈ $1.8M). Mitigate Seasonal Volatility: Smooth out the sharp month-to-month "sawtooth" profit shifts by introducing recurring revenue drivers, such as subscription models and targeted loyalty programs. Bridge Demographical Gaps: Launch entry-level premium packaging (smaller, lower-priced sizes) to capture early-career professionals in the underperforming 25-28 age bracket. Optimize Channel Ad Spend: Reallocate digital marketing capital to replicate Sydney's online framework in lagging European cities, while maximizing brick-and-mortar storefront space in international travel hubs like Paris.

Data Analysis Projects (Portfolio)

October 1, 2025 – April 1, 2026

Case study regarding Amazon India for to analyse Amazon Brazil's data to identify trends, customer behaviours, and preferences that could be leveraged in the Indian market using SQL. Engineered a relational analytical environment querying the multi- table Amazon Brazil (Olist) ecosystem, evaluating records across orders, payments, order items, products and customers. Identified product margin variations by writing conditional filters to isolate product categories (such as Smart Tech) displaying extensive price volatility (> 500 BRL variance). Programmed multi-level customer tiering logic to group buyer frequencies into New, Returning, and Loyal segments to map purchase health. Leveraged advanced PostgreSQL window functions (LAG(), SUM() OVER(), and DATE TRUNC()) to calculate Month-over-Month (MoM) growth rates and compute rolling, cumulative sales per category. Maximize Lifetime Value (LTV): Deploy automated milestone-based rewards targeting the identified "Loyal" customer segment to incentivize long-term retention. Dynamic Pricing Framework: Introduce bundled pricing strategies for highly volatile categories 500 BRL spread) to capture price-sensitive mid-market consumers Data-Driven Inventory Alignment: Utilize MoM rolling sales trends to dynamically rebalance seasonal inventory levels and reduce warehouse holding costs during lower-performing quarters.

NextLeap Data Analyst Fellowship (Top Fellow)

NextLeap

October 1, 2025 – Present