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Digvijay kasana

Analyst

About Digvijay

I turn messy, fast-moving data into clear decisions. My sweet spot is end-to-end work: scoping the question, cleaning the data, finding the signal in Python/SQL, and shipping something real—dashboards people use or a lightweight ML/LLM feature that actually moves a metric.Recent wins: built 7 executive KPI dashboards used by 12 stakeholders and shaved ~3 hours off weekly reporting; shipped a sales-commitment scoring system (LLM + ML on ADK Web) that flags over/under-committing with ~1.2s median latency and 89% CV accuracy (~220 pilot runs); designed a box-count predictor (Optuna-tuned RF, RMSE 0.30 on ~50k rows) and put it in users’ hands via Streamlit + ADK; prototyped an agent-to-agent Weather Q&A that answers in under a minute so planning teams don’t hunt across sites.What this really means is I can own the path from vague problem → measurable result: SQL and pandas/NumPy for analysis, scikit-learn/XGBoost for modeling, Streamlit/ADK for quick deploys, and Looker Studio/Tableau/Power BI to tell the story without the fluff. I care about clarity, reproducibility, and honest metrics—latency, accuracy, adoption.Toolbox: Python (pandas, NumPy, scikit-learn, XGBoost), SQL/BigQuery, Looker Studio, Tableau, Power BI, Streamlit, LLMs (prompting + eval), Google ADK Web, Git, basic Docker.Open to Data Analyst / Junior ML / Analytics roles. If you need someone who can ship a dashboard this week and an ML/LLM MVP next week, let’s talk.

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Skills

  • Analysis
  • Data handelling
  • Data warehousing
  • ETL
  • Excel
  • GCP
  • LLM
  • Looker Studio
  • Microsoft Office
  • NPL
  • PowerBI
  • Python
  • SQL
  • Statistics and probability
  • Tableau
  • transformer

Work Experience

01/2025 - 01/2026
Data Analyst
M2R Technomations

• Developed 7 executive KPI dashboards in Looker Studio adopted by 12 stakeholders; reduced weekly

reporting prep by ~3 hours via self-serve access.

• Delivered 4 production-ready classifiers/regressors across tabular, text, and image data (best models ~90%

accuracy) with clear eval and handover docs.

• Designed a box-count predictor (Optuna-tuned Random Forest, RMSE = 0.30 on ~50k rows) that

standardized decisions and cut packing mistakes by ~15%; deployed via ADK Web and Streamlit for direct

usage.

• Delivered a sales-commitment feasibility system (LLM + ML) with Google ADK Web: CV accuracy 89%;

prompt-driven scoring classifies over-committing / under-committing / good-to-go; median response ~1.2

second; ~220 pilot runs. Stack: Python, scikit-learn, ADK Web, Streamlit.• Built an AI Weather Bot (agent-to-agent) with ADK Web for real-time Q&A; less than 2 second end-to-end

responses, removing manual checks during planning.

08/2024 - 12/2024
Data Analyst Intern
XeliumTech Solutions

• Built 3 ML prototypes for ticket categorization and routing; reduced manual triage time by ~20% in pilot

evaluations.

• Produced 14 weekly trend reports on incident/request volumes; surfaced 3 recurring SLA-breach patterns

for ops leads.

• Ran SonarQube/OWASP ZAP reviews; analyzed scan data to surface top vulnerability categories and severity

trends; built a Power BI dashboard to present findings to stakeholders; documented 12 remediation actions

adopted by engineering for safer releases.

• Recommended 5 data-backed process tweaks (queues, hours, SLAs) that cut backlog by ~12% during trial.

01/2023 - 07/2023
Data Analyst Intern
Super Products

• Built competitor analysis using SQL/Excel (Power Query, pivots) across five competitors with pricing and

channel coverage.

• Ran marketing time-series tests in R on six months of data to guide budget allocation and campaign timing.

• Created Tableau views to track spend vs. ROI; shared weekly insights with leadership for decision-making.

03/2022 - 08/2022
Data Research Analyst Intern
Jangjeet Enterprises

• Applied basic ML in R on ~5,000 customer rows to support demand forecasting and inventory planning.

• Built a Power BI dashboard for four core metrics; assisted SQL/Excel migration of ~10,000 records with data-

quality checks.

• Co-authored three summary reports and dashboards for management decision-making.

Education & Training

2023-2024
Data Science
University of Nottingham
• CGPA: 7.12 · Focus: Machine Learning, Big Data, Time Series Analysis, Applied Statistics
2019 - 2023
Computer & Communication Engineering
Manipal University Jaipur
• CGPA: 7.46 · Relevant: Python, AI, Data Science, Data Mining, Web Programming

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