NumPy Jobs

I need an 8- to 10-page conference paper that presents a hybrid machine-learning Security Information and Event Management (SIEM) framework combining Random Forest and Isolation Forest for network-threat detection. The manuscript must follow either Springer LNCS or Scopus proceedings guidelines, complete with the correct template, figure sizing, and reference style. Core structure • Introduction and literature review that positions the problem, surveys recent SIEM advances, and justifies the hybrid approach. • Methodology and data analysis describing data-preprocessing, feature engineering, model building in scikit-learn, and experimental evaluation on publicly available cybersecurity datasets (e.g., CIC-IDS 2017, UNSW-NB15, or similar). • Conclusion and future work...

I’m building a research-grade quantum simulator in Python and need a robust codebase that can accurately model multi-qubit circuits, apply standard gate operations, and return state-vector or density-matrix outputs. Whether you prefer to work directly with NumPy/SciPy or leverage existing open-source frameworks such as Qiskit, Cirq, or QuTiP is completely up to you; the key requirement is clean, well-documented code that runs reliably under Python 3.11. Please provide: • A modular simulation engine capable of handling at least 10-15 qubits, with optional noise or decoherence modelling • A clear, Pythonic API for defining circuits, executing simulations, and extracting results (probabilities, expectation values, etc.) • Unit tests plus a concise README that cover...

I have already deployed a full Streamlit application that predicts loan approvals in real time (live demo: , source: ). The pipeline currently includes Logistic Regression, K-Nearest Neighbors, and Naive Bayes models with standard scaling and the usual EDA-driven feature engineering. What I want now is a measurable lift in overall model performance, with the F1-score as the guiding metric. Feel free to explore more advanced algorithms (e.g., Gradient Boosting, XGBoost, LightGBM, calibrated ensembles, or even a tuned version of my existing classifiers) as long as they integrate cleanly with the existing Python | Pandas | NumPy | Scikit-learn stack and can be surfaced through the current Streamlit front-end. Key points you should address • Re-examine preprocessing and feature sele...

Our historical sales data is signalling shifts that we don’t fully understand yet, so the priority is a diagnostic analysis that tells us not just what changed, but why it changed. The raw tables cover order details, customer attributes, product SKUs and daily revenue going back three years. Primary questions on the table: • How have sales trends evolved month-to-month and season-to-season? • Which customer segments are driving (or dragging) revenue, and how has their purchasing behaviour shifted? • Which products or product groups are over- or under-performing once promotions, returns and stock-outs are factored in? A clean, reproducible workflow in SQL, Python (Pandas, NumPy, Sci-Py) or R is essential so the team can rerun the analysis after future data drops...

I need clean, well-commented Python code that lets me back-test a momentum-based, algorithmic trading strategy on Indian stocks through the Angel One smart-API. At a minimum, the script should: • Pull historical equity data with the Angel API endpoints I already use in live trading. • Let me set adjustable momentum parameters (look-back window, ranking criteria, rebalance frequency, position sizing) from a single config section. • Generate a fast vectorised back-test, calculate P&L, drawdown, Sharpe and basic trade metrics, then output them to a tidy DataFrame and a couple of matplotlib / seaborn charts. • Stay modular so I can swap the data-loader or plug the core logic into my live trading script later. Acceptance criteria 1. I run one command and th...

I’m assembling a small pool of Python specialists who can jump in as on-demand interviewers for my hiring pipeline. Every candidate you meet will be applying for a data-science-focused role, so I need someone whose own background is genuinely senior-level in that space—think daily work with pandas, NumPy, scikit-learn, Jupyter notebooks, model deployment, and the usual mix of statistics and SQL that glues real projects together. Your task is to run one-hour coding-test sessions over Zoom or Google Meet. I supply the shortlist of applicants and the calendar invites; you supply the technical depth and the objective scoring. During each session you will: • present or share a prepared data-centric coding challenge (I’m happy to reuse a problem you already trust, or...