Pandas Jobs

I have a set of historical sales records—dates, units sold, revenue and a few categorical fields—that now need to drive reliable forecasts for the next two quarters. Your task is to clean the data, explore it for seasonality or anomalies, and then build a forecasting model that produces both point estimates and a visual projection of likely ranges. You may work in Python (pandas, statsmodels, Prophet), R, or even Excel/Power Query if you can justify the approach; what matters is a clear, reproducible pipeline. Along the way I expect concise commentary on assumptions, feature engineering steps and the accuracy metrics you use to evaluate performance. When you apply, attach a detailed project proposal: outline the methods you would test (e.g., ARIMA, exponential smoothing, mach...

I have a set of historical sales records—dates, units sold, revenue and a few categorical fields—that now need to drive reliable forecasts for the next two quarters. Your task is to clean the data, explore it for seasonality or anomalies, and then build a forecasting model that produces both point estimates and a visual projection of likely ranges. You may work in Python (pandas, statsmodels, Prophet), R, or even Excel/Power Query if you can justify the approach; what matters is a clear, reproducible pipeline. Along the way I expect concise commentary on assumptions, feature engineering steps and the accuracy metrics you use to evaluate performance. When you apply, attach a detailed project proposal: outline the methods you would test (e.g., ARIMA, exponential smoothing, mach...

I’m building a technology-driven initiative and need a seasoned data analyst to turn raw information into clear, actionable insight. The scope centers on three core services: • Data visualization – translate complex datasets into intuitive dashboards or charts so trends are instantly understandable. • Statistical analysis – run rigorous tests and descriptive stats to validate findings and quantify relationships. • Predictive modeling – create models that forecast key outcomes and recommend next steps. You’ll receive access to the datasets, along with context on our business objectives and any relevant KPIs. I’m open to your choice of platforms or languages—whether that’s Python (Pandas, Sci-Kit-Learn), R, Tableau, or anot...

I run a retail operation and want to turn the raw data we collect into clear, practical intelligence. The goal is to understand why customers buy, how their habits shift over time, and which factors drive repeat purchases so I can refine merchandising, promotions, and loyalty efforts. I can provide any combination of purchase history, demographic information, or website interaction data once we agree on the best approach. Your task is to explore the data, surface patterns, and translate them into an easy-to-read report with actionable recommendations—think trend visualisations, segment profiles, and concrete next steps I can implement right away. Please outline the methods and tools you prefer for data cleaning, exploration, and visualisation (e.g., Python/Pandas, R, Tableau, Power...

I have a collection of well-formatted CSV files and a live SQL database that I need to turn into clear, insightful visuals. The core of this engagement is data visualization—using Python to translate rows and columns into plots, dashboards, or interactive charts that quickly communicate trends and outliers to non-technical stakeholders. You will have direct access to the structured data; most of it is already clean, so the emphasis is on choosing the right libraries (Pandas, Matplotlib, Seaborn, or Plotly—whatever suits the story best), crafting reusable code, and presenting the results in a concise, easy-to-update format. If you are comfortable adding light data-cleaning steps or basic statistical summaries along the way, that flexibility is welcome, but not required. De...

I’m looking for hands-on guidance that will strengthen my technical toolkit specifically in statistical analysis. My aim is to move beyond simply running canned tests and instead understand the “why” behind each method, interpret results confidently, and apply them to real-world data sets. Here’s what I have in mind: • A clear learning plan that starts with core concepts (descriptive statistics, hypothesis testing, confidence intervals) and progresses into more advanced topics such as regression, ANOVA, and multivariate techniques. • Live or recorded walkthroughs of each concept using widely-used tools—Excel, Python (pandas, SciPy, statsmodels) or R—whichever you feel most comfortable teaching. • Practical exercises with sample or c...

I manage a fast-moving quantitative fund and I’m expanding the team on two fronts: deep fundamental research and purpose-built trading technology. RESEARCH ANALYSIS Your core mission is digging into papers and reports—academic studies, sell-side notes, 10-Ks, industry white papers—and pulling out the insights that move markets. I lean heavily on options and futures, so I’ll look to you to translate those findings into structured spread ideas, monitor relevant news flows, and keep a tight watch on sector-level fundamentals. Speed matters, but rigor matters more. This role requires daily updates and reports. SOFTWARE ENGINEERING In parallel, I need an algorithmic trading stack that can ingest live market data, run ML-driven models, execute orders, and log performanc...

I have several CSV files that contain a mix of text and numeric columns. I need a Python script that reads these files, cleans obvious issues (extra whitespace, stray symbols, inconsistent number formats, blank rows) and then applies flexible filtering rules I can adjust myself later. Key points • Source files are plain CSV; no database or API connection is involved. • The data is mixed, so the script must recognise both text and numeric fields and keep their types intact after processing. • Filtering is the only required transformation for now—think along the lines of dropping rows that fail certain thresholds, keeping records that match specified keywords, or combining multiple filter conditions with AND/OR logic. Deliverable 1. A well-commented Python ...

I am ready to move an idea from concept to a fully-deployed Python solution and need a developer who writes clean, well-structured, thoroughly documented code. The scope spans the full life-cycle: feature development, rigorous debugging, test coverage that inspires confidence, and finally packaging and deployment to a live environment. Because the exact business logic will be shared after kickoff, versatility is important. Familiarity with automation workflows, data-analysis pipelines, RESTful or GraphQL APIs, and modern AI/ML libraries (Pandas, NumPy, scikit-learn, TensorFlow, or similar) will allow you to hit the ground running, yet please stay pragmatic—choose only what the project truly needs. From you I would like: • A concise project timeline that breaks work into clear ...

I am building a full-featured data analysis tool and am looking for a Python developer who can take it from concept to deployment with clean, well-structured, and fully tested code. Scope of work The core of the project is a pipeline that handles • data cleaning • data visualization • statistical analysis All stages should be written in Python using Pandas for wrangling, Matplotlib for visual output, and Scikit-learn where modelling or advanced statistics are required. I want the codebase organised for long-term maintainability, with clear documentation, type hints where useful, and unit tests that cover critical paths. Beyond core development I will need help with debugging, automated testing, and packaging/deployment (a Docker image or similar solution that le...

I am building an end-to-end Python solution that automatically ingests, processes, and reports on financial data pulled from CSV files, SQL databases, and live APIs. Clean, well-structured, and easily maintainable code is essential because the system will be extended over time. Scope of work You will take the project from initial design through deployment. That includes setting up the data-collection pipeline, writing transformation and analysis modules, implementing robust error handling and logging, building unit / integration tests, and preparing a production-ready deployment script or container. The stack is entirely Python, so I expect idiomatic use of tools such as pandas, SQLAlchemy, asyncio, and, where it adds value, lightweight AI/ML components for anomaly detection or forecast...

Cloud Pricing Validation & Test Automation Framework Designed and developed an end-to-end Python automation framework to scrape and validate cloud pricing data from AWS and Azure platforms. Built Selenium-based UI automation using the Page Object Model (POM) and PyTest framework to validate pricing calculators, search functionality, filters, and region selection. Implemented API validation using the requests library to compare pricing data with scraped UI data. Automated data storage in SQLite and performed SQL-based validation for duplicates, missing values, and historical price comparison. Generated HTML execution reports, detailed logs, CSV/Excel summaries, and dashboards to monitor pricing changes and regression results. Integrated modular architecture with reusable utilities, con...

I have a raw dataset that needs to be explored, cleaned, and turned into clear business insights. Excel is my preferred environment, so formulas, PivotTables, Power Query, and easy-to-refresh dashboards should be at the heart of the solution. If you see value in quickly prototyping advanced models in Python (Pandas, Scikit-learn) or R and then translating the results back into Excel, I’m open to that—but the final deliverable must remain fully functional inside an .xlsx file. What I expect: • A well-structured Excel workbook that cleans the data, performs the analysis, and presents results on a summary dashboard. • Concise written explanations of key findings, assumptions, and recommended next steps. • Any supporting scripts or notes if you use supplementary...

I have a two-part project that blends data science with creative rendering. First, I need to mine our operational data to uncover clear, actionable insights that will streamline internal workflows. You’ll have direct access to our raw logs and performance metrics; from there, I’m expecting clean data pipelines, exploratory analysis, and models or dashboards that highlight where we can cut cycle time, reduce error rates, and better allocate resources. Python (Pandas, scikit-learn), SQL, or a similar stack is ideal, but I’m open to the tools you find most efficient as long as the findings are reproducible and clearly documented. Second, I want a series of polished 2D and 3D character renders that visualize key personas involved in those optimized processes. Think engagin...