Analitică predictivă Jobs

I have a time-series collection of remote sensing data—specifically multi-spectral satellite imagery—and I need an AI model that can reliably pinpoint and quantify changes that occur from one acquisition date to the next. The end goal is an automated workflow that flags where and when significant alterations appear, whether those are shifts in vegetation, newly built structures, or other surface transformations. To give you an idea of scope, the raw scenes are already orthorectified and radiometrically corrected. What I’m missing is the machine-learning layer that will take two (or more) aligned images, learn the patterns of normal variability, and then output clear, georeferenced change masks and summary statistics. Key expectations • Model architecture, trainin...

Options Trading Bot — Real-Data Validation & Fix. Why this bot is currently shut down — stated plainly This is a real-money automated options trading bot (Schwab broker). It is currently fully halted by two independent safety locks (a hard kill switch and a max-drawdown gate), both enforced in code and verified directly against live server logs. It stays off until someone proves — with real data, not guesses — that it can make money without blowing up the account. The real trading record, from the account’s own ledger: • 25 real trades: 6 wins, 19 losses • Realized P&L: -$1,527.85 on a $1,885.57 starting account (81% drawdown) • The losing streak that did the real damage: six straight losing trades in August with zero wins in between, c...

I have a full extract of our retail inventory history and I want to turn it into a living model that tells me exactly when, what, and how much to reorder so we stop tying up cash in slow-moving items while never running out of the fast movers. Your task is to dive into the inventory data, uncover the patterns that drive demand, and deliver a predictive engine focused on stock level optimization. Here’s how I picture the engagement: • Data assessment & preparation: explore the raw tables, flag gaps or anomalies, and structure the dataset so the model can consume it without manual fixes each cycle. • Model development: build and tune a demand-driven algorithm (time-series forecasting, probabilistic safety-stock calculations, or a hybrid you prefer) that outputs optima...

I have a full extract of our retail inventory history and I want to turn it into a living model that tells me exactly when, what, and how much to reorder so we stop tying up cash in slow-moving items while never running out of the fast movers. Your task is to dive into the inventory data, uncover the patterns that drive demand, and deliver a predictive engine focused on stock level optimization. Here’s how I picture the engagement: • Data assessment & preparation: explore the raw tables, flag gaps or anomalies, and structure the dataset so the model can consume it without manual fixes each cycle. • Model development: build and tune a demand-driven algorithm (time-series forecasting, probabilistic safety-stock calculations, or a hybrid you prefer) that outputs optima...