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I also build practical Python workflows for everyday business operations: spreadsheet cleanup and repeatable reports, AI-assisted lead prioritization with human-reviewed reply drafts, and small API/webhook connections. Each project starts with one agreed input, output, and acceptance checklist.
Excel/CSV reporting: https://contra.com/s/7XkrBeAe-excel-and-csv-cleanup-and-reporting-automation
AI lead qualification: https://contra.com/s/PLqOeNoW-ai-lead-qualification-and-follow-up-drafts
API/webhook integration: https://contra.com/s/FWOXOe7c-fast-api-and-webhook-integration
Python task automation: https://contra.com/s/N6qX2AUX-python-automation-for-repetitive-business-tasks
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Excel & CSV Cleanup and Reporting Automation by Harsh Vishwakarma
Read more about Excel & CSV Cleanup and Reporting Automation by Harsh Vishwakarma on Contra.
The network for creativity
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𝐉𝐨𝐛 𝐡𝐮𝐧𝐭𝐢𝐧𝐠 𝐨𝐧 𝐡𝐚𝐫𝐝 𝐦𝐨𝐝𝐞
B2 English, based in Argentina. Payment preferably in USDT. Remote only. Quite a combination, right?
I built an AI-assisted job-search system around Freehire and Hirify. Both platforms offer official API/CLI access for automation, which makes them much easier to connect to an agent workflow. Simple Python scripts collect job listings and run the first pass of filtering against strict rules.
Then agents double-check the requirements, match them against my experience, and tailor my CV to each role. They highlight relevant projects and technologies I've actually worked with. Final review and approval stay with me.
𝐀𝐬 𝐨𝐟 𝐒𝐞𝐩𝐭𝐞𝐦𝐛𝐞𝐫 𝟐𝟔:
✅ 20 applications sent;
✅ 8 HR screenings passed;
✅ 1 video screening completed and submitted.
🚀 Technical interviews are still ahead.
I'm not ready to write off the market. Even with my constraints, things are moving.
A job search deserves 𝐚𝐧 𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐚𝐩𝐩𝐫𝐨𝐚𝐜𝐡 𝐭𝐨𝐨: clear rules, automation, and checks on the results.
Messy Data In. Clearer Decisions Out. 📊
Over the past year, I've noticed most businesses don't struggle because they lack data — they struggle because that data never turns into a decision.
That's the gap I work in:
→ Cleaning and analyze data with Python & R
→ Turning findings into investor-ready pitch decks
→ Polishing the final write-up so nothing gets lost in translation
If your team has data sitting in spreadsheets with no story attached, or a pitch deck that needs to actually land — that's exactly where I can help.
Open to projects. Let's talk 👇
Behind every clean chart is a messy spreadsheet 🧹
This is what a real data cleaning job looks like before it becomes an elegant bar chart — duplicate rows, missing values, inconsistent formatting, all sorted out with Python and Pandas.
#collage attempt: a look inside the data analyst's actual desk, not just the pretty output.
