GAGAN DEEP PAHUJA

Strategic Profitability & Financial Insights

• Delivered strategic profitability analysis across M&A transactions including sell-side deals (supporting companies seeking acquisition) and rapid-turnaround buy-side analyses (validating target metrics, conducting reconciliations, and preparing financial insights summaries).
• Solely responsible for managing the complete data lifecycle—ingesting and processing raw financial data, identifying data gaps or missing KPIs, and mapping KPIs across multiple datasets for consistency.
• Conducted robust outlier checks, validation routines, and resolved anomalies by engaging directly with stakeholders and partners; proposed issue-resolution strategies when inconsistencies were found.
• Standardised and structured large datasets for downstream financial slicing and insight generation, tailored to deal-specific requirements.
• Conducted financial due diligence and business performance assessments, providing key insights to support investment decisions.
• Performed customer and product cohort analysis:
• Customer Cohort: Evaluated customer retention and net revenue lifecycle, identifying patterns in customer stickiness and attrition over time.
• Product Cohort: Analysed product launch performance by category, assessed relevance to target demographics, and tracked whether products were discontinued or remained revenue-generating.
• Executed price-volume (PV) analysis to segregate revenue impact from pricing vs. volume trends.
• Generated New, Lost, and Existing (NLE) product and customer analytics at a fiscal-year level, aligned with cohort evaluations.
• Led inventory analysis, addressing complexities distinct from P&L analysis by applying tailored assumptions to handle non-additive inventory values.
• Managed end-to-end data processing—cleaning, standardisation, and validation—to deliver output via Excel, Power BI, and PowerPoint.
• Mentored associates in both technical development and strategic insight generation.

Automation & Development (Python)

• Developed multiple GenAI POCs, including:
• Data Profiler Tool: Built using Azure OpenAI GPT-4o with custom multi-agent framework for SQL processing, graph generation, and insights extraction. The tool summarizes input data, identifies key KPIs, suggests use cases, recommends cleaning steps, and allows users to query the data to generate datasets and graphs. UI built in Streamlit with SQL database backend. Actively used by the US KPMG tech team; Phase 2 development in progress.
• ESG Intelligence Assistant: Built for ESG team to fetch ESG regulatory documents and disclosures using LangChain with custom agents for tokenizing PDFs and extracting relevant insights. Automatically organizes outputs by geography and time period, storing results in SharePoint. Python-based implementation is currently in production; Phase 2 is ongoing.
• Macro-Economic ETL Tool: Created for UK FDD team to extract macroeconomic data from multiple sources, transform it into structured Python dictionaries, and provide an interactive UI for querying and downloading data in Excel format.
• Built Python-based solutions to automate data extraction, transformation, and validation across diligence and analytics workflows.
• Developed scripts using Xlwings and REST APIs to streamline integration with Power BI dashboards for a major Pharma client.
• Created a Tkinter-based Python app for website analytics and dash-boarding (Dash Plotly), earning a Lean Six Sigma Yellow Belt.
• Designed scraping tools for platforms like Google, Amazon, Trustpilot, Glassdoor, and Indeed to support real-time diligence and validation.

Digital Due Diligence & Consumer Analytics

• Engaged in multiple DDD projects, focusing on analysing target firms and competitors via their social media presence across various segments, including:
• Voice of the Customer (VOC): Categorised and performed sentiment analysis on customer reviews sourced from platforms like Google, Amazon, Trustpilot, and Yelp, identifying thematic areas such as price and quality
• Social Media Analytics: Conducted similar VOC analysis on social media chatter from platforms like Facebook, Twitter, Reddit etc, considering factors like gender and demographics for earned mentions Evaluated growth metrics through owned mentions of social media handles for target/peers through impressions, reach, and engagement.
• Website Analytics: Examined traffic drivers and engagement metrics of target/peers' websites, utilising factors like SEO, organic/paid visits, unique visitors, and dwell time.
• Google Trends Analytics: Analysed keyword-specific growth trends of target/peers over time using Google Trends data

• Conducted parcel distribution analysis for a Belgian postal firm, identifying inefficiencies across distribution centres and workforce planning.
• Used statistical methods and AWS SageMaker for model building and outlier treatment (Z-score).
• Predictive Modelling & Backend Development
• Built ML models for ETA estimation and theft detection in logistics using Python.
• Developed REST APIs for model querying and backend scripts for ingestion, processing, and storage.
• Integrated MongoDB, HBase, MySQL for data storage and Redis for performance optimisation.