Freelancers using Python in New York

Freelancers using Python in New YorkFreelancers using Python in New York

Projects People

Web&Mobile Developer

Web&Mobile Developer

Cover image for SaaS Platform Development

SaaS Platform Development

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Cover image for Built a domain-specific AI chatbot

Built a domain-specific AI chatbot

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Cover image for Tech Job Board Platform

Tech Job Board Platform

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AI Avatar Creator Mobile App

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Avid Software Engineer, Data Engineer and Technical Writer

New to Contra

Avid Software Engineer, Data Engineer and Technical Writer

Cover image for Data, AI, Software, Technical Writing
𝗬𝗢𝗨𝗥

Data, AI, Software, Technical Writing 𝗬𝗢𝗨𝗥 𝗦𝗔𝗟𝗘𝗦 𝗗𝗔𝗧𝗔 𝗔𝗟𝗥𝗘𝗔𝗗𝗬 𝗞𝗡𝗢𝗪𝗦 𝗪𝗛𝗜𝗖𝗛 𝗖𝗨𝗦𝗧𝗢𝗠𝗘𝗥𝗦 𝗔𝗥𝗘 𝗔𝗕𝗢𝗨𝗧 𝗧𝗢 𝗟𝗘𝗔𝗩𝗘. Most owners check that money came in. Few ask what the numbers say about next month. I ran a 90-day Dead Stock Check on Horti Supplies data (about $14,500 in sales). In plain sight: 👤 𝟲 𝗼𝗳 𝘁𝗵𝗲 𝘁𝗼𝗽 𝟮𝟱 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀 𝗾𝘂𝗶𝗲𝘁𝗹𝘆 𝘀𝘁𝗼𝗽𝗽𝗲𝗱 𝗯𝘂𝘆𝗶𝗻𝗴: about $550 a month walking away. $𝟵𝟰𝟬 𝘀𝘁𝘂𝗰𝗸 𝗼𝗻 𝘁𝗵𝗲 𝘀𝗵𝗲𝗹𝗳 - 8 products unsold for 30 to 83 days made 2.4% of sales but held 22% of stock value. 📉 𝗧𝗵𝗲 𝟯𝗿𝗱 𝗯𝗲𝘀𝘁-𝘀𝗲𝗹𝗹𝗶𝗻𝗴 𝗽𝗿𝗼𝗱𝘂𝗰𝘁 𝗿𝗮𝗻𝗸𝗲𝗱 𝟮𝟭𝘀𝘁 𝗳𝗼𝗿 𝗽𝗿𝗼𝗳𝗶𝘁. 🕑 𝗧𝗵𝗿𝗲𝗲 𝘀𝗹𝗼𝘄 𝗮𝗳𝘁𝗲𝗿𝗻𝗼𝗼𝗻 𝗵𝗼𝘂𝗿𝘀 - brought in 7% of sales; rent and staff costs didn’t drop. 𝗧𝗿𝘆 𝘁𝗵𝗶𝘀 𝘁𝗼𝗱𝗮𝘆 (𝟭𝟬 𝗺𝗶𝗻𝘂𝘁𝗲𝘀, 𝗻𝗼 𝘀𝗼𝗳𝘁𝘄𝗮𝗿𝗲): export last month’s sales, sort by profit, and circle anything that earned almost nothing but took up shelf space. 𝗧𝘄𝗼 𝘄𝗮𝘆𝘀 𝗜 𝗰𝗮𝗻 𝗵𝗲𝗹𝗽: 1️⃣ 𝗧𝗵𝗲 𝗗𝗲𝗮𝗱 𝗦𝘁𝗼𝗰𝗸 𝗖𝗵𝗲𝗰𝗸: send one month of sales data (names removed is fine) and get a one-page report on where cash is stuck, which customers are drifting, and 3 actions for this week. 🎁 The first 10 get it free, in return for sharing the anonymised result as a case study. 2️⃣ 𝗖𝗼𝗺𝗶𝗻𝗴 𝘀𝗼𝗼𝗻, 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝗟𝗼𝗼𝗽: upload a CSV or SQL export and get a named diagnosis (Customer Churn, Dead Stock, Demand Forecast) plus a visual report. 𝗖𝗼𝗺𝗺𝗲𝗻𝘁 “𝗖𝗛𝗘𝗖𝗞” 𝗳𝗼𝗿 𝗮 𝗳𝗿𝗲𝗲 𝗗𝗲𝗮𝗱 𝗦𝘁𝗼𝗰𝗸 𝗖𝗵𝗲𝗰𝗸, 𝗼𝗿 “𝗟𝗢𝗢𝗣” 𝗳𝗼𝗿 𝘁𝗵𝗲 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝗟𝗼𝗼𝗽 𝘄𝗮𝗶𝘁𝗹𝗶𝘀𝘁. I’m Moses, a data engineer and founder of Closertune. Do you know an owner who says, “Sales are slow, but I don’t know why”?

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Cover image for I recently built an interactive

I recently built an interactive Power BI sales dashboard tracking $17.91M in total sales across supervisors, managers, and order years. I then designed the data model and DAX measures behind the KPI cards, gauge, and trend visuals, and built drill-down views: sales by supervisor and year, quantity split by manager, and order volume by year. I used combo visuals (donut, gauge, bar, and line charts) on one page so stakeholders can get the full picture without switching reports. I also focused on a clean, single-screen layout that non-technical stakeholders can read at a glance. #Microsoft Power BI #Daxko Operations #Data Visualization #Data Modeling #Business Intelligence

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Cover image for Building the technical foundation for

Building the technical foundation for Otonix, an AI-native IT Operations Platform I worked on the engineering side of Otonix, an autonomous IT operations platform built on agent-first, task-specific AI agents that monitor, triage, remediate, and escalate IT operations, instead of a chatbot bolted onto legacy RMM/PSA tooling. What the build involved: 🔹 Architecting the platform around four core agent capabilities: Monitor, Triage, Remediate, and Escalate. 🔹 Building the policy-guardrail layer that constrains automated actions and gates anything touching regulated systems or irreversible changes. 🔹 Implementing the audit trail: a signed, searchable, exportable log of every agent decision and action. 🔹 Working with a signal-ingestion pipeline (endpoints, servers, network, IoT) feeding into the triage/remediation flow. Python/FastAPI, Node.js, React, PostgreSQL, Docker, n8n, Kubernetes I build backend systems and full-stack platforms for technical products. Especially where reliability, auditability, and guardrails matter as much as the automation itself. If you're building something in a similar space, let's talk. 📩 mosesmorrisdev@gmail.com | github.com/Moses-Morris

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Cover image for This is a web design project for an Institution.

This is a web design project for an Institution.

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Building time series forecasting pipelines

New to Contra

Building time series forecasting pipelines

Cover image for Quantura: Building an Inspectable Forecasting Platform

Quantura: Building an Inspectable Forecasting Platform

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Cover image for Forecast API, SDKs & OAuth

Forecast API, SDKs & OAuth MCP — Quantura I built a shared Search → Download → Forecast API for Quantura so developers can move from a discovered market or dataset to historical observations and asynchronous forecast results. The integration includes JSON/CSV exports, job status handling, provider and model metadata, scoped API access, and OAuth sign-in for MCP clients. Python and TypeScript SDKs plus a CLI provide reusable integration examples. I also organized the public RapidAPI listing into clear endpoint groups, documented usage and authentication, and added examples for scheduled forecasting workflows. This work is useful for teams connecting forecasts to their own apps, notebooks or AI tools. Explore the API: https://rapidapi.com/tamzid2001/api/quantura2

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Cover image for Forecasting pipelines across market and

Forecasting pipelines across market and economic data Built as part of Quantura, my forecasting and research product. I implemented a shared data workflow for Alpaca, Dukascopy, Gemini, Treasury Fiscal Data, World Bank Data360 and BigQuery public datasets. The work covers provider discovery, series selection, timestamp and unit normalization, observed-history downloads, six forecast quantiles and scheduled execution. Data coverage and model requirements remain explicit, and missing observations are not invented. Deliverables include Python/TypeScript integrations, JSON/CSV exports, asynchronous job handling and chronological historical evaluation. I can build a similar workflow for your supported time-series data, with clear inputs, milestones and operating costs. Explore: https://quantura.studio/

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Cover image for Scout: AI-assisted forecast research —

Scout: AI-assisted forecast research — Quantura I built Scout inside Quantura to help users interrogate the history and forecast they are actually viewing. Three contextual question cards organize the experience around Forecast, History and Evidence. Users can inspect median changes and uncertainty ranges, ask about observed highs and lows, and review data sources and units. Structured answers use the selected observations and forecast values as context. The interface also supports chart annotations, user notes, saved conversations, and named CSV previews before forecasting. The goal is to make a forecast easier to inspect, explain and revisit. My work combines TypeScript backend logic, a JavaScript research interface and interactive charting. Explore: https://quantura.studio/forecasting

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Linux, DevOps & GitHub Actions Engineer

New to Contra

Linux, DevOps & GitHub Actions Engineer

Cover image for Linux Fleet Readiness: Risk Audit & Decommission Planning

Linux Fleet Readiness: Risk Audit & Decommission Planning

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Cover image for Guarded Fleet Change: Policy-Gated Ansible Rollouts

Guarded Fleet Change: Policy-Gated Ansible Rollouts

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Cover image for Runner Fleet Reliability Platform

Runner Fleet Reliability Platform

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Cover image for Built a dependency-free Python CLI

Built a dependency-free Python CLI that prioritizes synthetic incidents, classifies severity, and recommends evidence-based diagnostic next steps.

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A/B Testing Statistician for SaaS & E-commerce Teams

12: Followers

A/B Testing Statistician for SaaS & E-commerce Teams

Cover image for CVR Rose 8%, but Profit per Visitor Fell 6%

CVR Rose 8%, but Profit per Visitor Fell 6%

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Cover image for Five Arms, One Nominal Winner, Zero Valid Launches

Five Arms, One Nominal Winner, Zero Valid Launches

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Cover image for Daily Peeking Turned Noise Into a 19.7% False-Win Rate

Daily Peeking Turned Noise Into a 19.7% False-Win Rate

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Cover image for Brazilian E-Commerce: 3% Repeat, 8% Late

Brazilian E-Commerce: 3% Repeat, 8% Late

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AI Engineer & Backend Developer | Python, FastAPI & Flask

AI Engineer & Backend Developer | Python, FastAPI & Flask

Cover image for Managing local logistics with fragmented

Managing local logistics with fragmented spreadsheets, messaging apps, and phone calls usually leads to zero real-time visibility over fleet availability, missed maintenance schedules, and lost waybills. I built FleetMS to streamline end-to-end delivery operations for small and medium-sized logistics enterprises (SMEs). It gives operations teams a central dashboard to monitor real-time vehicle utilization and delivery lifecycles, while giving drivers an uncluttered interface to execute daily waybills with zero overhead. What it handles: Central Operations Console: Real-time visibility into active deliveries, vehicle status, and 30-day volume trends. Driver Dispatch View: Clear pickup and drop-off coordinates, itemized package breakdowns, and direct call actions built for fast field execution. What part of this workflow causes you the most friction tracking vehicle availability in real time or keeping drivers updated on multi-stop waybills?

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Cover image for MaxCinema: Flask Backend & Automated Content Management

MaxCinema: Flask Backend & Automated Content Management

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Cover image for Building AI for healthcare leaves

Building AI for healthcare leaves zero room for error. I’m currently collaborating with an incredible team on Raphald AI, a medical detection application. Building the systems for a project with stakes this high is a massive reminder that the underlying backend architecture matters just as much as the machine learning model itself. When integrating diagnostic AI, your API endpoints cannot drop requests, and your database workflows demand absolute integrity. You aren't just passing JSON payloads; you are handling critical, real-time workflows where stability is non-negotiable. Engineering these systems continues to shape my approach to building robust Python backends. If you are developing a product that requires reliable AI integration or rock-solid FastAPI infrastructure, check out the newly updated services on my profile. Let's build something that works when it counts.

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1.1K

Cover image for Developed an AI-powered home-service marketplace

Developed an AI-powered home-service marketplace that connects customers with service contractors and supports the workflow from service requests and contractor matching through quoting, booking, escrow payments, and job completion. The platform also includes real-time messaging, AI-assisted service intake and quoting, contractor management features, public service estimation tools, and an AI voice-dispatch workflow for contacting matched contractors.

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Jonathan Williams

proNew York, USA

Full-Cycle Web Development & Data Scraping Expert

$1k+: Earned

4x: Hired

5.0: Rating

11: Followers

Full-Cycle Web Development & Data Scraping Expert

Cover image for Sports Betting Recommender System

Sports Betting Recommender System

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Cover image for Automated Data Scraping Solution - Con Edison

Automated Data Scraping Solution - Con Edison

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Cover image for Event Registration App (Covid-19)

Event Registration App (Covid-19)

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Crafting Your Digital Presence 💻 😄

5.0: Rating

5: Followers

Crafting Your Digital Presence 💻 😄

Cover image for Z-Define: The Ultimate Gen Z Dictionary

Z-Define: The Ultimate Gen Z Dictionary

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Cover image for Vengo Films Website & CRM Automation

Vengo Films Website & CRM Automation

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Cover image for OHOS Media: NYC Media Production Portfolio Website

OHOS Media: NYC Media Production Portfolio Website

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Cover image for Website for Cannes Nominated Film

Website for Cannes Nominated Film

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