We are building an early-stage real estate data platform that collects, cleans, enriches, and serves public-record and legal-notice data for real estate investors and professionals. This is not a greenfield build. We already have an existing backend repo with API routes, database models, migrations, scraping workers, tests, Docker configuration, and cloud deployment pieces. We need a strong backend engineer who can step into the existing system, understand what is working, identify what is risky, and help us get the backend stable enough for launch. The right person is practical, scrappy, and comfortable working in a startup environment where the goal is not perfection. The goal is to find the highest-leverage path to a reliable product. The platform involves: -Public-record and legal-notice data -Property data enrichment -API endpoints used by a frontend application -Data quality, reliability, and launch-readiness Current Backend Stack The backend is built primarily in Python and includes: -FastAPI -SQLAlchemy and Alembic -Postgres / Google Cloud SQL -MongoDB helper/caching layer -Scraping and ETL pipeline for public-record and legal-notice data -Playwright/Patchright-based scraping -reCAPTCHA-aware scraping workflows -LLM-based data extraction / AI-assisted parsing of unstructured notice data -Pydantic models -Google Cloud integrations: Cloud Run, Cloud Scheduler, Pub/Sub, Secret Manager, Cloud Storage, Artifact Registry -Docker -Pulumi infrastructure-as-code -GitHub Actions CI/CD -pytest, Ruff, uv You do not need to be world-class in every tool listed above, but you should be strong enough in Python backend systems, scraping/data pipelines, and cloud deployment to quickly understand the architecture and make sound technical decisions. What We Need Help With We need someone who can: -Review and understand the current backend architecture -Stabilize and improve the scraping / ETL pipeline for public-record and legal-notice data -Make sure public-record and legal-notice data is collected, parsed, stored, and served correctly -Improve backend APIs used by the frontend -Improve data quality checks for incomplete, missing, or inconsistent property records -Build and maintain property enrichment workflows using external data sources -Help design database models for richer property history and event tracking -Improve LLM-assisted parsing of unstructured legal notice data where appropriate -Debug deployment, CI/CD, Cloud Run, and infrastructure issues -Improve logging, error handling, monitoring, and observability -Strengthen test coverage where it matters -Help document the backend so future developers can contribute -Coordinate with our frontend developer to support product launch -Help prioritize backend work based on launch impact, data reliability, and technical risk Who This Is For You are likely a strong fit if you: -Like working inside existing codebases -Can diagnose messy systems without needing everything rewritten -Think in practical tradeoffs, not just ideal architecture -Are comfortable with incomplete documentation -Have experience with scraping/ETL workflows and unstructured data extraction -Can explain technical risks clearly to a non-technical founder -Prefer shipping useful improvements over debating perfect abstractions -Are willing to own outcomes, not just complete assigned tickets Who This Is Not For This is probably not the right fit if you: -Only want clean, fully documented codebases -Prefer to rebuild from scratch by default -Need enterprise-level process before making progress -Are an agency sending rotating developers -Only want tightly defined tickets with no ambiguity -Are uncomfortable with scraping, data quality, or production debugging Hiring Process We want to keep the hiring process practical and focused on real work. 1. Initial Screening We will review your proposal, background, and screening question responses. 2. Real-World Technical Scenario Strong candidates may be asked to respond to a specific backend issue from our current roadmap. We are looking for how you think, what tradeoffs you notice, and how clearly you communicate. 3. Paid Finalist Review A small number of finalists may be invited to complete a paid review of the existing backend codebase before any larger implementation work begins. Budget / Working Style We are an early-stage company and are looking for a practical, startup-minded developer. This is a paid contract role, but we are not looking for enterprise-agency rates. We value clear communication, efficient execution, and someone who can help us prioritize the highest-leverage backend work first. The first paid technical review may be structured as a fixed-price milestone. Continued implementation work may be hourly or milestone-based depending on fit. Long-Term Opportunity Our goal is to find someone who can become a long-term backend partner for the product, not just complete isolated tickets. For the right person, there may be an opportunity to grow into a technical lead / backend ownership role with additional upside tied to company performance. We are looking for someone who wants to help take a real product to market, but the initial engagement will be paid, scoped clearly, and focused on proving mutual fit.

We are looking for a strong software engineer who can build practical automation systems using AI, APIs, and modern development tools. This role is for someone who can take messy business workflows, understand the goal, and build working systems that save time, reduce manual work, and improve execution. You should be comfortable building automations, integrating tools, working with APIs, writing clean code, and using AI tools like OpenAI, Claude, or similar models to create useful business applications. What You’ll Work On You will help build and improve systems such as: AI-powered research and data extraction workflows CRM and sales process automations Email, spreadsheet, and database automations Internal tools and dashboards API integrations between business software Web scraping and data enrichment workflows when appropriate AI agents or assistants that help with repetitive business tasks Automation around deal screening, reporting, lead research, and document creation Ideal Candidate We are looking for someone who is practical, fast, and can figure things out without needing step-by-step instructions. You should have experience with: Python and/or JavaScript APIs and webhooks OpenAI, Claude, or other LLM APIs Automation tools like Zapier, Make, n8n, Airtable, Google Sheets, HubSpot, Salesforce, or similar Databases such as PostgreSQL, Supabase, Firebase, or similar Basic front-end or internal tool development Web scraping, data cleaning, and structured data workflows GitHub and clean documentation What Matters Most We do not need someone who only talks about AI. We need someone who can actually build. The right person should be able to: Understand a business process quickly Recommend the simplest technical solution Build fast prototypes Turn prototypes into reliable workflows Communicate clearly Document what was built Improve systems over time Nice to Have Experience with any of the following is a plus: Private equity, M&A, finance, or investment workflows Deal sourcing or lead generation systems CRM automation Data enrichment tools AI research agents Browser automation Cloudflare, AWS, Google Cloud, or similar infrastructure Engagement This will start as a part-time project-based role, with the potential to become ongoing if the work is strong. Estimated workload: 5 to 15 hours per week to start. To Apply Please include: Examples of automations or AI tools you have built The tech stack you usually work with A brief explanation of how you would approach automating a messy manual workflow Your hourly rate Your availability Please do not send a generic application. If your response looks copied and pasted, it will be ignored.

Here’s what I’m doing: I’m looking to make our lead research process better and get it fully automated. Here’s the process as it stands now: 1. We start with a list of companies and their websites. 2. For each one: scrape their site for shipping facility locations — warehouses, DCs, manufacturing plants. Check their locations/facilities pages and their careers page. Job postings for warehouse, forklift, shipping & receiving, production roles confirm an active facility and usually give the address. Careers pages are inconsistent — some are static HTML, some run through Workday, Greenhouse, iCIMS, or other ATS platforms that don’t always let scrapers in. I need someone who’s dealt with that before and knows how to handle it, not just scrape the easy ones and skip the rest. 3. Identify contacts matching these titles: Transportation Manager, Director of Transportation, Logistics Manager, Director of Logistics, Traffic Manager, Senior Transportation Manager, Senior Logistics Manager, Logistics Sourcing Manager, Logistics Procurement Manager, Transportation Procurement Manager, Transportation Sourcing Manager. 4. Score every contact for whether they’re actually still there — not just whether they show up in a database. Apollo and ZoomInfo are full of people who left or retired years ago but still show as active. The scorecard has to catch that before it goes any further. 5. Enrich the contacts that pass: email and phone. Phones: enriched numbers are usually garbage with no way to verify them. Scrape the company website for the corporate number instead. No 800 numbers — those are dead-end customer service lines. I want a local number so I can call and ask whether the person is still there. I know distinguishing a corporate number from an 800 number on a scraped page isn’t always straightforward — some sites only list the 800 number, some bury the local number in a footer or contact page. Tell me how you’d approach that. Emails: fine as-is — enrich, run through NeverBounce. Non-negotiable: every contact comes out with a score and the specific reason behind it. No black box. I need to see why someone scored high and why someone else scored low. End state: I drop in the list, it runs, output comes back scored and enriched with facility locations attached. Fully automated, repeatable.

I need a Discord bot built that posts positive expected value (+EV) sports betting picks to a channel, plus esports match schedules/scores. I have the full technical spec already written — APIs picked, EV formula defined, architecture outlined. This is integration work, not research. What's provided: Working API keys for The Odds API (sports odds) and PandaScore (esports stats) — I'll provide on hire Complete EV calculation formula (Python, ready to use) Full architecture spec (polling schedule, caching approach, bookmaker lists) Core deliverables (must-have, fixed price): Discord bot that connects to my server Scheduled polling (1-2x daily, not live) of The Odds API for sports moneylines + player props EV calculation comparing soft-book odds (DraftKings/FanDuel/Caesars) against Pinnacle as the sharp reference Caching layer (database) so the bot never calls the API live per Discord command Bot posts flagged +EV picks to a designated channel, above a configurable EV threshold PandaScore integration for esports match schedules/results posted to a separate channel Basic error handling with retry/backoff (no runaway API costs) Documentation: how to run it, how to add new sports/leagues, how to change the EV threshold Stretch goal (only if time allows within budget — not required): OddsPapi integration for esports moneyline EV detection (this API is unverified/free-tier, so treat as experimental) Tech preferences: Python or Node.js, whichever you're stronger in. Open to your hosting recommendation (needs to run 24/7 cheaply). Budget & Payment Structure: $500 fixed price, split into milestones Milestone 1 ($150) — Foundation: Discord bot connects to server, The Odds API integration pulling live sports odds data, caching layer working. Paid on demo of working data pull + bot online in server. Milestone 2 ($200) — Core Logic: EV calculation implemented and verified accurate against manual spot-checks, picks posting to Discord channel automatically on schedule. Paid on demo of at least 3 correctly-calculated +EV picks posted live. Milestone 3 ($150) — Esports + Polish: PandaScore esports schedule/results integration, error handling/retry logic, documentation delivered. Paid on final delivery + handoff call. To apply: Tell me your estimated hours for each milestone, and confirm you've worked with Discord bots + REST API integrations before.

Senior Python Dev to Productionize a PPTX Medical Report Automation Prototype Overview We are a medical imaging report provider (Brain & Spine Injury Imaging Experts). We have a working Python prototype that auto-populates branded PowerPoint (.pptx) medical report templates and exports them to PDF. We need a senior developer to productionize it into a reliable, deterministic, well-documented tool. This is an automation/scripting engagement, not a slide-design job. What you'll build Harden and refactor our existing Python + python-pptx prototype into production-quality code. Fill locked-layout PowerPoint templates from structured input data (JSON) — text fields, findings lists, figure captions, measurements, and image placement. Generate required charts programmatically (e.g., NeuroQuant regional-percentile bar chart) rather than pasting static images. Export pixel-consistent PDFs (Aspose.Slides acceptable) that render identically on every run. Build a validation script that checks each generated report against required rules (footer text, page count, required fields, figure/label integrity) and flags deviations. Adapt the same engine to a second report type (Thoracic Spine) after the primary TBI report is accepted. Determinism requirement The tool must produce byte-for-byte consistent output across runs on the same input — same fonts, same layout, same positioning. Please describe in your proposal how you guarantee deterministic rendering and handle font embedding/substitution. Known Sample Drift — Footer Must Be Corrected The attached sample reports contain intentional footer inconsistencies that must NOT be reproduced. The signature-block footer currently varies across pages (some pages show only "Board Certified Radiologist," others add "Fellowship Trained in Musculoskeletal Imaging"). This drift is a known defect in the samples. The AUTHORITATIVE footer is the version we will supply in the spec sheet, and it must be applied identically on every page of every report. Your validation script must detect and reject any page whose footer does not exactly match the approved footer text. Do not treat the samples as the source of truth for footer content — treat the supplied spec as authoritative. Compliance / data handling This project involves medical reports. All development and testing will use de-identified sample data and dummy files only; you will not receive real patient PHI. A mutual NDA is required before we share the prototype code, templates, or licenses. Final report delivery to our HIPAA-compliant ShareFile account is handled with sandbox/test credentials during development; production keys are connected by us. Optional Phase 6 — Automated ShareFile Delivery Add a step that uploads the final PDF to a specified folder in our HIPAA-compliant ShareFile (Citrix) account via the ShareFile REST API, using a configurable case-to-folder mapping. Build/test with a non-PHI test folder and scoped test credentials; production credentials supplied and connected by client. Include setup instructions. Milestones (fixed price, ~$5,000 total, negotiable) Paid proof-of-concept: regenerate one approved sample report from our prototype on your environment, matching our reference output (small milestone — go/no-go gate). Production refactor of the fill engine and template handling. Deterministic PDF export + font handling. NeuroQuant chart generation + figure/label integrity. Validation script (footer, page count, required fields, drift detection). Spine report adaptation. (Optional Phase 6: ShareFile delivery.) A completion bonus applies for full delivery within 10 days of the awarded start. Required skills Python, python-pptx, Aspose, Microsoft PowerPoint, automation, data visualization, JSON data handling. API integration (ShareFile REST) a plus. To apply, please answer: Have you built PowerPoint/PPTX automation with python-pptx and/or Aspose? Share an example. How do you guarantee deterministic, pixel-consistent PDF output (fonts, layout)? Have you integrated the ShareFile (Citrix) API or similar OAuth-based file-storage APIs? Estimated timeline to complete Milestones 1–5. Attachments: De-identified sample reports (TBI and Spine) are attached for reference. Prototype code, real case data, and Aspose licenses will be provided after the NDA is signed.

We are seeking a skilled freelancer to build a social listening tool that can scrape data from various online sources. The tool should be able to handle large volumes of data and provide insights into social media trends and user interactions. The ideal candidate will have experience in data scraping and social media analysis, and be able to develop a tool that is efficient and reliable. Prior to contract, I'd like to have a 15 minute discussion that gets into specific needs and uses for this tool

Overview I am looking for an experienced Python developer to build a stand-alone desktop research application for futures trading strategy analysis. This is not an automated trading bot and does not require live trading execution. The purpose of this software is to replace manual backtesting and allow systematic research of Opening Range Breakout (ORB) strategies. The application will allow a trader to quickly test strategy variations, compare results, and identify robust parameters without manually running hundreds of backtests. Accuracy of results is the highest priority. Project Goal Build a desktop application where the user can: Select a futures market Load historical data Configure ORB strategy parameters Run single tests or multiple parameter combinations Analyze results Compare experiments side-by-side Save research results The software should be simple and user-friendly. Platform Stand-alone desktop application. Primary requirement: Windows Desired: macOS compatibility The user should not need: TradingView Excel FX Replay Coding knowledge The software should open like a normal desktop application. Supported Markets (Version 1) The architecture should support: Nasdaq Futures NQ MNQ S&P 500 Futures ES MES The system should properly handle: Tick size Tick value Contract specifications The design should allow additional futures markets to be added later. Data Requirements Historical Data Integration with: Databento API Requirements: 1-minute historical data User-selectable date ranges Ability to build higher timeframe candles from 1-minute data Supported research candles: 1 minute 3 minute 5 minute 10 minute 15 minute ORB Strategy Engine Standard ORB User can select: 1 minute 3 minute 5 minute 10 minute 15 minute opening range Dynamic ORB (Anchor ORB) User can select: 1 minute 3 minute 5 minute 10 minute 15 minute Logic: The first candle that closes outside the opening range becomes the new ORB anchor. The closing price of that candle becomes the reference level for entries. Entry Types Version 1 supports: Breakout entry Dynamic ORB anchor entry Stop Loss Testing User can test: 25% 33% 50% 66% 75% 100% Stop size is based on ORB size. Profit Target Testing The software must support testing multiple R targets: From: 0.5R to 10R In: 0.5R increments Example: 0.5R 1R 1.5R 2R etc. Risk Management Support: Fixed Dollar Risk Example: $100 $250 $500 Percentage Account Risk Example: 0.5% 1% 2% Filters ORB Size Filter User selectable: Minimum: 0.10% Maximum: 2.00% Day of Week Filter Allow testing: Monday Tuesday Wednesday Thursday Friday News Filters Option to exclude: High-impact economic news days FOMC days Federal Reserve Chair speech days Research Engine The software must support: Single Backtest Run one specific strategy configuration. Multi-Variable Testing Allow combinations of: Market ORB duration ORB size Entry type Stop size Profit target Day filters News filters Example: Test: 10 ORB sizes 6 stop sizes 20 profit targets Multiple markets Automatically generate and run experiments. Parameter Locking Important feature: The user must be able to lock certain parameters while testing others. Example: Lock: Entry type Risk model Optimize: ORB size Stop Target This prevents unnecessary over-optimization. Results Dashboard Display: Performance Metrics Net Profit Profit Factor Expectancy Win Rate Total Trades Average Winner Average Loser Maximum Drawdown Largest Winning Streak Largest Losing Streak Charts Required: Equity Curve Drawdown Curve Experiment Comparison Allow side-by-side comparison. Example: Strategy A vs Strategy B Compare: Parameters Profit Factor Expectancy Drawdown Trade count Win rate Saving Research Users should be able to: Save experiments Reopen experiments Save notes Technical Preferences Preferred: Python backend Open to developer recommendations for: Desktop framework Database Architecture Experience preferred with: Financial applications Backtesting systems Time-series data Quantitative research tools Important Developer Qualifications Please have experience with: Event-driven backtesting Historical market data Avoiding look-ahead bias Accurate trade simulation Parameter optimization This project is research-focused. A simple candle backtester is not sufficient. Application Requirements Please provide: Examples of similar work GitHub or portfolio links if available Recommended technology stack Estimated timeline Fixed-price estimate Budget Expected MVP range: $4,000–$7,000 (depending on experience and recommended architecture) This project may expand into future versions after successful completion. Final Note The goal is to build a reliable research tool that allows systematic testing of futures strategies. The first version should prioritize: Accuracy Simplicity Ease of use Clean architecture for future expansion

Looking for a Python developer with sports analytics experience to build a college football grading engine. The full specification includes 34 custom metrics, requiring a deep understanding of football analytics and data modeling. The project involves creating a grading engine that can process large datasets efficiently and accurately. Ideal candidates will have experience with data visualization and machine learning.

I am looking for an experienced Python developer or algorithm specialist to rigorously test and validate a few scripts that I have. The script locates stocks that have been trending for 2-3 days on the MACD zero line PRE BREAKOUT . Your primary goal will be to stress test the script, identify edge cases, very output accuracy, and ensure robust performance under various conditions. Code review: Evaluate the existing code base for efficiency, security, and best practices. Functional Testing: run the script against sample data sets to verify output, accuracy Edge case testing: intentionally push the algorithm to its limits to find potential bogs, bottlenecks or failure points. Documentation: provide the detailed report of your findings, including reproduction steps for any bogs and recommendations for optimization. Requirements: proven experience in algorithm, testing the bargaining and performance optimization. Strong proficiency in python and relevant testing frameworks. Familiarity with API’s or database Excellent and analytical skills and attention to detail. To apply: please submit a brief proposal, including: 1. Examples of past projects where you tested de BAIRD or optimize an algorithm. 2. Your preferred mythology for testing this type of script.. 3. Your estimated turnaround time for this project..