NumPy Jobs

Work Zone Digital Twin — Pipeline Testing, Validation & Bug Fixes Project Overview We have a completed 11-stage Python pipeline that converts dashcam video and GPS/IMU telemetry into a top-down map of highway work zone assets (cones, drums, signs). The pipeline is built and running end-to-end. We need an engineer to test it thoroughly, validate outputs, and fix remaining issues. Pipeline Summary 11-stage offline pipeline: Stage 1-3: Telemetry preparation, keyframe extraction, video/GPS sync Stage 4: COLMAP sparse SfM reconstruction Stage 5: Metric scale alignment (GPS + physical reference measurement) Stage 6-7: Manual and auto annotation tools (browser-based) Stage 8: Multiview triangulation of asset 3D positions Stage 9: Taper ordering and work zone measurements Stage 10: Grou...

I have a raw CSV export of customer-level transactions from our retail platform and I need clear, data-driven insight into which products are really driving revenue. The goal is simple: surface product-level sales trends so I can see which SKUs consistently outperform and which ones lag behind, all viewed through the lens of revenue generated rather than just unit counts. You’re free to structure the workflow as you see fit, but I expect the core analysis to happen in Python, leveraging pandas and NumPy for data wrangling and aggregation. I’ll supply the dataset along with a brief data dictionary; you return a well-commented Jupyter notebook (or .py script) that: • Cleans and normalises the raw data • Calculates revenue by product over selectable time windows &b...

I have a dataset made up entirely of categorical variables and I want to understand the hidden relationships inside it. The task is strictly exploratory: I am not asking for predictive modelling, only a deep dive that surfaces meaningful patterns and trends. What I expect from you • Clean the data where needed so that the exploratory work is reliable. • Use suitable techniques for categorical exploration—cross-tabulations, chi-square tests, association rules, clustering on encoded variables, or any other method you feel is insightful. • Present the findings in clear, non-technical language supported by concise visuals (bar charts, heat-maps, mosaic plots or similar). • Provide a short, well-commented notebook or script (Python with Pandas, NumPy, SciPy, s...

I’m looking for a seasoned Python professional to step in and keep my existing codebase running smoothly. The core of the project is a collection of data-processing scripts; several of them expose lightweight endpoints through Flask, so familiarity with that micro-framework is essential. Here’s what I need from you: trace and squash the occasional bug, clean up sections that have grown messy, and tune performance when large datasets start to slow things down. I’d also appreciate guidance on best practices—whether that means introducing unit tests, improving logging, or suggesting smarter dependency management—so the project remains healthy as it evolves. You’ll work from a private Git repository, submit pull requests with clear commit messages, and pro...

I am building a cinematic blast sequence and need solid data-driven support rather than eye-balling particle emitters. The idea is to run an AI/ML simulation that predicts how metal, glass, concrete, wood and plastic fragments fly away from the detonation point, then turn those numbers into clear data-visualisation charts I can hand directly to the FX team. Scope • Direction, speed and distance must be predicted separately for each material class, following the colour code I already use on set (red = metal, blue = glass, grey = concrete, orange = wood, green = plastic). • The charts have to highlight the mean and standard deviation for every variable so the artists immediately see the “typical” trajectory as well as the natural spread. Other metrics such as perce...

I have a single CSV file that contains a mix of numerical columns and categorical labels. Before I can move on to analysis, the data needs a solid clean-up: remove or impute missing values, fix obvious data-entry anomalies, standardise text categories, and ensure each field is in the correct type. Once the dataset is tidy, I want to explore it visually. I am especially interested in bar charts, line graphs and scatter plots that help surface the main trends and relationships hidden in the numbers and categories. Feel free to suggest any additional plots that would add real insight; however, the three mentioned above are the minimum I need delivered. Python with pandas, NumPy and either Matplotlib or Seaborn is perfectly fine, but I am open to R or another proven toolset if you prefer...

I need a Python-based trading algorithm that trades both the Nifty and Bank Nifty indices. The code should run locally on Python (feel free to lean on pandas, NumPy, TA-Lib, backtrader or similar libraries) and must be able to import and work with historical market data only—no live feed is required for this milestone. Here is what I expect: • A clean, well-commented Python script (or notebook) that ingests historical data, generates trade signals, executes the logic, and outputs detailed performance metrics and an equity curve. • Clear instructions on how to map the code to CSVs or API endpoints I already use for historical NSE data. • A short README explaining any configurable parameters so I can tweak settings for further experiments. Back-testing accuracy, ...