Freelance Data Scrapers in Burbank
Freelance Data Scrapers in Burbank
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Matin Pichvai
Los Angeles, USA
Finance pro building models, dashboards & investor decks.
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Finance pro building models, dashboards & investor decks.
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A working demonstration of how AI integration replaces hours of finance work with minutes. Three real workflows shown end-to-end with before/after data: Transaction categorization. Messy bank/card transactions (wires, AMEX charges, vendor descriptions) auto-categorized, vendor-cleaned, and pivoted into a category summary. Manual: 2–3 hours for 200 transactions. AI-assisted: 5 minutes. Investor commentary drafting. Raw quarterly performance data (NAV, returns, watchlist movement, pipeline) turned into polished investor letter copy. Manual: 1–2 hours of writing per quarter. AI-assisted: 10 minutes of editing. Portfolio company summarization. Unstructured sponsor letters and press releases parsed into a structured tracking table with revenue, margins, leverage, and status flags. Manual: 30–45 min per company. AI-assisted: instant. The integrations are built INTO existing Excel workflows — no new tools for the team to learn. Two common patterns: copy/paste with Claude, or direct API integration via VBA / Office Scripts. The "after" sheets look like normal Excel because they are; AI just did the manual work. Illustrative samples built for portfolio purposes. Happy to scope an automation for your specific finance workflow.
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Series A pitch deck for a healthcare growth equity raise. A 12-slide investor presentation built for a multi-state dermatology platform raising Series A capital. The deck covers the investment thesis, market opportunity, unit economics, growth strategy, financial projections, and use of funds — structured the way institutional investors actually read pitches. Built from my background in audit at PwC and as a controller at a major alternative asset manager, where I've spent years on both sides of investor materials: building them for portfolio companies and reviewing them as a capital allocator. That dual perspective shapes how I structure narrative, defend numbers, and anticipate diligence questions. Mock content built for portfolio purposes. Happy to discuss approach, layout choices, or adapt the structure for your raise.
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Quarterly investor report for a private credit fund. A full LP-facing reporting package covering fund-level performance (NAV, returns, IRR), portfolio composition, credit metrics (LTV, DSCR, weighted yield), and manager commentary. Built in Excel with linked source data, formula-driven KPIs, and an output layer formatted to read like a polished investor letter. The work most LPs see from their GPs is exactly this kind of report. My background — controller at a major alternative asset manager, audit at PwC and CohnReznick — is on the production side: building the systems that make these numbers reliable, then presenting them in a format institutional investors trust. That means the report ties cleanly, the underlying logic is auditable, and the commentary aligns with the data instead of contradicting it. Illustrative sample built for portfolio purposes. Happy to adapt the structure for a specific fund, asset class, or LP reporting cadence.
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A fully integrated three-statement model — income statement, balance sheet, and cash flow — built for a growth-stage company across a four-year forecast horizon. Working capital, debt, and tax schedules sit on dedicated tabs and flow into the consolidated outputs. Built so the balance sheet balances every period and cash reconciles cleanly between the indirect cash flow and the cash line on the balance sheet. Sensitivity toggles on revenue growth, gross margin, and capex assumptions let users stress-test the forecast without breaking the model. Audit-grade discipline throughout: no hard-coded outputs, clear input/calculation/output separation, formula transparency on every line. My background is in audit at PwC and as a controller at a major alternative asset manager — so I've spent years reviewing models for what breaks under pressure, then building them to a standard that holds up to that review. Illustrative sample built for portfolio purposes. Happy to adapt for your specific business — SaaS, services, consumer, marketplace, or other.
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Balaj Khalid
Los Angeles, USA
Expert Data Analyst and Machine Learning Engineer
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Expert Data Analyst and Machine Learning Engineer
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Google Shopping Price Scraper
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9
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Fake News Detection with DistillBERT
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21
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Predicting Earthquake Magnitude: Insights from 28 Years of Seism
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Michael Smith
Burbank, USA
AI Operations & Startup Systems Consultant
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AI Operations & Startup Systems Consultant
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Google Data Certification
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StreamStash is a fintech platform built for digital creators — musicians, streamers, vloggers, influencers, and online entrepreneurs — to organize, track, and access their earnings across multiple platforms in one place. The idea behind StreamStash is simple: Creators today make money from many different sources: Streaming revenue Brand deals YouTube monetization TikTok payouts Affiliate income Merchandise sales Donations and subscriptions Sponsorships But all of those payments arrive at different times, on different platforms, with different payout rules. StreamStash acts like a financial command center for creators. What It Does Unified Creator Dashboard Creators can connect multiple income platforms and view: Total earnings Pending payouts Monthly revenue trends Platform performance Estimated future income Faster Access to Earnings One of the platform’s goals is reducing the long wait creators face before getting paid. StreamStash is designed to support: Instant payouts Early payout access Revenue advances Flexible withdrawal systems Especially for independent creators who rely on consistent cash flow. AI Financial Assistant A future-facing part of StreamStash is AI-powered creator finance support. The AI layer could: Forecast upcoming income Analyze spending patterns Suggest budgeting strategies Predict slow revenue periods Recommend growth opportunities Help creators understand taxes and business finances Not replacing accountants — more like giving creators a smart financial operations assistant. Why It Matters Most creator tools focus on content creation. StreamStash focuses on the business side of being a creator. That matters because a lot of talented creators: don’t understand cash flow, get paid inconsistently, struggle with taxes, or can’t scale because their finances are fragmented. StreamStash aims to make creators feel less like freelancers surviving month-to-month and more like actual digital businesses. Positioning The platform sits somewhere between: creator economy tools, digital banking, creator analytics, and fintech infrastructure. Think: “Plaid + Stripe + creator analytics + AI finance copilot for creators.” And frankly, the creator economy still has a giant hole here. Everybody wants creators to make content. Very few companies are helping creators build financial stability.
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We’re building products that blend AI, creator tools, and workflow automation. Currently, we’re developing platforms that prioritize secure collaboration, creator monetization, and AI-driven operations. We’re eager to connect with founders, startups, and teams who need assistance in rapidly transforming their ideas into functional systems. In the fast-paced world of technology, execution is paramount.
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Created a modern AI workflow and startup operations graphic designed to showcase operational systems, automation strategy, reporting visibility, and scalable business workflows for startups and growing companies. The concept focuses on improving efficiency, reducing manual work, and organizing operations through AI-assisted systems and structured workflows.
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Rubayet Bin Mujahid
Los Angeles, USA
Web Developer creating responsive websites
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Web Developer creating responsive websites
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Competitor Price Tracker
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Systems Modeling Chatbot
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2
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Roommate Expense Splitter
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Folio — Lead-generating portfolios for freelancers
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4
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