预测分析 Jobs

Build real-time fraud scoring for Swift Tech Co.'s FinTech clients, models that have to catch fraud without declining a real customer's legitimate transaction. Responsibilities • Build and tune real-time transaction fraud-scoring models • Design rule-based fallback logic for cases a model shouldn't decide alone • Investigate false positives and tune thresholds against real transaction data • Work with compliance staff on FINTRAC-aligned reporting for confirmed fraud

I’m building an application whose core value is actionable forecasts generated by AI-driven predictive analytics. The idea is to feed historical data into a well-designed pipeline, surface trends, and return clear recommendations or probabilities to end-users inside a clean interface. I have not locked the project into a single sector yet, so whether the data turns out to be time-series sensor readings, customer behaviour logs, or financial records we can choose the most suitable modelling approach together. Here’s how I picture the flow: first, you and I refine the problem statement and decide what questions the model must answer. Next comes data ingestion and cleaning, followed by feature engineering and model selection—think scikit-learn, XGBoost, TensorFlow, or which...

I need help turning a sizable collection of unstructured finance-related text and document feeds into an accurate, production-ready predictive model. My goal is to spot patterns that reliably forecast the target variables I will share once we start (think risk scores and market-driven KPIs). The raw inputs arrive as news articles, earnings transcripts, and PDF reports, so the first step is a solid NLP-centric pipeline that cleans, tokenises, and extracts meaningful features. I work in a regulated environment, which means the code must be reproducible, well-commented, and easy to audit. Python with libraries such as spaCy, Hugging Face Transformers, pandas, and scikit-learn—or an equivalent stack—makes the most sense to me, but I’m open to a better suggestion as long as i...

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...