Numpy jobs

...with live order execution and performance reporting. All elements—data cleaning, feature engineering, back-testing, walk-forward validation, risk controls, and real-time monitoring—should sit inside one coherent codebase so I can flip from historical tests to live trading with minimal re-configuration. Implementation Notes • Python is my preferred language; common libraries such as pandas, NumPy, TA-Lib, and matplotlib are ideal. • Please structure the project so that broker connections (for example Interactive Brokers, Zerodha Kite, or a similar API) can be swapped out without touching strategy code. • Configuration files or a simple UI for parameter tweaks (MA lengths, RSI thresholds, candlestick filters, position limits) will keep things fle...

... Your job is to design, implement, and validate an anomaly-detection model that flags faulty nodes, suspicious traffic, or out-of-range measurements in near-real time. Feel free to select the technique that best fits the data volume and complexity; I am open to statistical, classic machine-learning, or deep-learning pipelines as long as the final system is transparent and reproducible. Python (NumPy, Pandas, Scikit-Learn, TensorFlow/PyTorch) is preferred for ease of deployment, though MATLAB is acceptable if it yields stronger results. The deliverables should be: • Well-commented source code and any trained models • A concise README explaining setup, model logic, and how to retrain with fresh data • A short performance report summarising detection accuracy...

...(PyClone-VI), VAF, gene expression, mutant allele expression - Normal brain expression (GTEx), CNS GO flag, microglia signature - Human proteome homology (BLAST), self-antigen flag (IEDB) - HLA allele frequency (AFND), HLA category - Peptide length, hydrophobicity, net charge, stability - Assemble 23-feature matrix Stage 4 - Model Development - Install Python 3.10+ (scikit-learn, xgboost, torch, pandas, numpy) - Train XGBoost (n_estimators=300, max_depth=6, learning_rate=0.05) with 5-fold CV - Train Random Forest (n_estimators=500, max_features='sqrt') with 5-fold CV - Train Neural Network (23 -> 64 -> 32 -> 1, dropout 0.3, Adam, BCE loss) - Apply early stopping on validation loss - Train logistic regression meta-learner on base learner predictions - Define Co...

I have several datasets coming in from different systems where the numeric fields are stored as plain ASCII files in some cases and UTF-8 exports in others. My goal is to run solid, reproducible data analysis on them, but first they need to be cleaned and standardised. Here is what I need from you: • Build a repeatable cleaning pipeline in Python (Pandas, NumPy) that detects the file encoding, converts everything to UTF-8 where necessary, and validates that all numeric columns are truly numeric—no stray characters, misplaced separators, or locale-specific quirks. • Where the raw files already sit in SQL Server, create T-SQL or Python-based routines that achieve the same checks and corrections in-database so I can schedule them in an Agent job. • Document...

...review pull requests as they land. Let me know your approach, estimated timeline, and any data or access you need to get started. here is the tech stack Frontend: HTML5, CSS3, Vanilla JavaScript Backend: Python, FastAPI Web Server: Uvicorn Database: SQLite with WAL mode Background Jobs: APScheduler Email Integration: Microsoft Graph API, Outlook AI Integration: OpenAI API Candidate Matching: NumPy, rank-bm25, Custom Python Rules Resume and Document Processing: PyMuPDF, pdfplumber, python-docx Excel Exports: openpyxl Data Validation: Pydantic Authentication: bcrypt, python-jose Notifications: Telegram Bot API Automation Backend: Node.js, Browser Automation: Playwright Package Management: pip, pnpm Windows Automation: PowerShell, Batch Scripts, Windows Task Scheduler Testing: p...

...their quality, handling augmentations, labeling inconsistencies, and packaging everything into tidy, well-documented datasets that flow straight into our training scripts. Most tasks live in the data-preprocessing and cleaning stage, so you should be comfortable writing reproducible code for resizing, normalization, class-balancing, and automated sanity checks. We currently use Python with Pandas, NumPy, OpenCV, and sometimes Albumentations; if you have a favorite toolkit that speeds things up, I am open to it as long as it’s clearly explained and container-ready. Once a batch is processed, you’ll push it to our Git-based repo and drop a short markdown report that lists what changed, any edge cases you spotted, and the commands needed to reproduce the run. Clean com...

...applied aerodynamics • Hands-on CFD/aerodynamic analysis experience • Strong Python skills • Experience with CLI, scripting, configuration-based, or programmatic engineering tools • Ability to troubleshoot and validate CFD results Relevant Tools: Experience with OpenFOAM, SU2, Gmsh, FEniCSx, Elmer, CalculiX, SciPy, or equivalent programmatic engineering software is valuable. Python experience with NumPy, SciPy, pandas, Matplotlib, Jupyter, solver APIs, or scientific libraries is also relevant. Selection Process: 1. Application and screening questions 2. ~30-minute AI interview 3. Technical assessment, if required 4. Hiring manager review Selected candidates should be ready to start within 24–48 hours after onboarding. Interested candidates can appl...

I have a clean, ready-to-use Iris dataset and need a complete Support Vector Machine classification pipeline built around it. The job is straightforward: tune an SVM (scikit-learn or similar) to sepa...• A short write-up (markdown inside the notebook is fine) explaining why the chosen kernel and parameters work best, plus any insights you notice in the feature space. • Saved model file so I can deploy or reload it quickly later. I’ll provide the CSV as soon as we start, and I’m happy to test the notebook in my own environment, so please stick with widely supported libraries such as pandas, NumPy, scikit-learn and matplotlib/seaborn for visuals. If you’ve built SVM classifiers before, this should be quick; quality, clarity and reproducibility matter mo...

...reliability/confidence score to its assessment. sample dataset : i run the progam using cli, json output is fine All results should be written to a concise text report that I can easily parse or forward—feel free to suggest the most convenient plain-text structure. You’re free to use OpenCV, scikit-image, NumPy, or any other well-supported libraries so long as installation remains straightforward (pip/conda). Robustness matters more than raw speed: lighting changes, slight exposure shifts, and minor stitching artefacts are present in some files, so the algorithm should tolerate them without false positives. If you have experience matching viewpoints in panorama or VR imagery—and can demonstrate a repeatable

...to guide me through it, review my work, unblock me when I get stuck, and run mock interviews at the end. This is not a "teach me from scratch with your own materials" role — I have the curriculum already mapped out day by day. I need someone who can teach to that curriculum, adapt it if needed, and keep me accountable to the timeline. What the Program Covers Python for data analysis — pandas, NumPy, data cleaning/wrangling (McKinney's Python for Data Analysis + DataCamp courses) Git & GitHub — version control fundamentals, branching, pull requests SQL — from basic SELECT statements through joins, aggregation, and window functions (DataCamp SQL Fundamentals, Teate's SQL for Data Scientists, and a MySQL-specific applied-projects cou...

...more job-market records using Python, Pandas and NumPy. Made an automated ETL/data preprocessing pipeline to take care of data cleaning, changing data making it all the same and checking if it is all the same. Added a PostgreSQL (Neon) database to keep data in a good way and help with machine learning work. Did analysis of the data. Looked at the features to find what helps predict salary. Trained a Random Forest regression model to guess salaries. Found that employee_residence is one of the things to use for guessing salaries. Built a FastAPI backend with places to get analytics and predictions. Linked the machine learning backend to a React frontend to show information and results in a way. Technologies: Python, Pandas, NumPy, Scikit-learn, Random For...

I need a concise, end-to-end Python workflow that turns a small customer transaction & engagement dataset into clear insights about how people behave on our platform. The data will require thorough cleaning and validation, thoughtful feature engineering, and sensible normalization before any analysis begins. Once the data is tidy, please explore it with Pandas and NumPy, surface the most useful behavior patterns, then build a light predictive component focused on customer segmentation. A handful of well-chosen Matplotlib charts should illustrate the key trends and support the final narrative. Deliverables • A single, well-commented Python script or Jupyter Notebook that runs start to finish without manual tweaks • The cleaned, processed version of the dataset s...

...single, logically organized notebook or script—that walks through data cleaning, imputes missing values using the mean or median as appropriate, and then performs a concise exploratory analysis. Once the data is ready, generate a set of bar charts, line charts, and scatter plots with Matplotlib (seaborn is fine if you prefer) so the insights stand out clearly. Please rely on Pandas for wrangling, NumPy for any numerical work, and Matplotlib for the visuals. Keep the logic straightforward and easy for another developer to follow. Deliverables • Python source code (with comments and logical sections) • Cleaned CSV dataset • Folder or embedded output containing the bar, line, and scatter plots • A short written summary (a few paragraphs or a markdow...

...using formulas, PivotTables, charts, sorting, filtering, conditional formatting, and data validation. Power BI – Create interactive dashboards and reports to visualize KPIs, trends, sales, inventory, and business performance. SQL – Query, filter, join, aggregate, and analyze data stored in relational databases. Python – Use Python for data analysis, automation, data manipulation, and reporting. NumPy & Pandas – Clean, transform, analyze, and manipulate structured datasets efficiently. Data Visualization – Convert complex data into easy-to-understand charts, graphs, dashboards, and reports. Statistical Analysis – Analyze patterns, trends, averages, distributions, and relationships within datasets. E-commerce Analytics – Analyze product...

... handles database connection/ingest, trains, evaluates, and outputs coefficients • Inline docstrings and a concise README explaining how to run and extend the code • A short validation script or notebook that demonstrates coefficient calculation, R², and residual plots on a sample pull from the database Acceptance criteria The model must compile and run with only standard libraries plus NumPy. Results on the sample data should match analytical solutions within a small numerical tolerance, and your class interface should allow easy extension to multiple regression later....

...Build and maintain data ingestion and transformation pipelines. * Develop, deploy, and monitor machine learning models. * Clean and organise messy data from different sources. * Translate business needs into practical data or ML solutions. * Document systems, processes, and technical decisions. * Work independently and collaborate in English and Spanish. Requirements * Strong Python, pandas, NumPy, and scikit-learn skills. * Experience with PyTorch or TensorFlow. * Advanced SQL knowledge. * Experience with Airflow, Prefect, Dagster, or similar tools. * Knowledge of at least one cloud platform. * Fluent spoken and written English and Spanish. Experience with Spark, dbt, Docker, MLOps, LLMs, or an early-stage startup is helpful but not required. To apply, send your CV and a sho...

...non-Bayesian and expected Bayesian estimators, assess them through a thorough Monte-Carlo study, and demonstrate their practical value on real data. You will write the complete manuscript and supply every line of code that leads to the reported numbers and figures. My preferred workflow uses both R and Python, so feel free to split the analysis between, for example, tidyverse / rstan in R and numpy / scipy / matplotlib in Python, provided the results match. The paper should follow the structure: Abstract, Literature Review, Methodology, Conclusion, and be typeset in LaTeX (with a clean .tex source) so that equations and proofs are presented clearly. Originality is critical; no AI-generated text or derivations will be accepted. The mathematical sections must show each step o...

...region, channel, units, revenue, and date; if you discover gaps or outliers along the way, feel free to flag them so we can address them together. Your task is to explore the data statistically and visually, highlight the drivers behind revenue fluctuations, surface any seasonality or regional differences, and call out under-performing product lines. You are welcome to work in Python with pandas, NumPy, seaborn / matplotlib, or R and SQL—whatever makes the workflow efficient—as long as the final insights are presented in a clear, non-technical format my broader team can understand. Deliverables: • Reproducible analysis file (notebook, script, or spreadsheet) • Concise report summarising findings, implications, and next steps (PDF or Word) • Well-l...

...region, channel, units, revenue, and date; if you discover gaps or outliers along the way, feel free to flag them so we can address them together. Your task is to explore the data statistically and visually, highlight the drivers behind revenue fluctuations, surface any seasonality or regional differences, and call out under-performing product lines. You are welcome to work in Python with pandas, NumPy, seaborn / matplotlib, or R and SQL—whatever makes the workflow efficient—as long as the final insights are presented in a clear, non-technical format my broader team can understand. Deliverables: • Reproducible analysis file (notebook, script, or spreadsheet) • Concise report summarising findings, implications, and next steps (PDF or Word) • Well-l...

...tests/ + ; top-level artifacts key; no tests/Dockerfile; no extraneous files. The 27 rubric criteria from , grouped as: what the task tests, the instruction, the verifiers, the solution, metadata, and hygiene — all gating. Environment/packaging — the exact pinned python:3.13-slim-bookworm digest from ; single image for agent and verifier with numpy==2.3.2, pytest==8.4.1, pytest-json-ctrf==0.3.5 baked; never COPY solution/ or tests/; the reward contract (/logs/verifier/, stripped environment, agent code only in a child process); the resource numbers from task.toml. Functional requirements of this specific task — the SFT trainer package, as the normative schema, three fixed-point modules, one loud bait defect in batching

I have a very large customer dataset from my restaurant operations that I want explored in depth. The sole focus is on purchase-pattern insights—how, when, and what guests buy—so I can refine promotions, optimize inventory, and improve table turnover. You’ll start by cleaning and structuring the raw files, then move into quantitative analysis using whatever mix of Python (Pandas, NumPy), SQL, or a BI tool such as Tableau/Power BI you prefer. I expect clear visualizations and a concise written summary that highlights actionable findings—think cohort trends, time-of-day spikes, day-of-week swings, and any unexpected correlations you uncover. Deliverables • Cleaned, documented dataset (CSV or database dump) • Interactive dashboard or set of c...

I have a dataset that’s raising more questions than answers and I need a seasoned pair of eyes to perform thorough diagnostic analysis. Using Excel for quick exploratory checks and Python (Pandas, NumPy, Matplotlib, or similar) for deeper dives, your job is to uncover the root causes behind the trends and anomalies I’m seeing, then translate those findings into clear, practical recommendations I can act on immediately. Here’s what I’m expecting: • A brief plan outlining the analytical approach you’ll take once the data is shared. • Cleaned, well-documented code/notebooks plus any supporting Excel files. • A concise report (slides or PDF) that walks through the key drivers you discovered, the evidence behind them, and suggested next...

I need help turning a raw set of customer records stored in my SQL database into clear, actionable insights. The work starts with efficient SQL queries to pull and tidy the data, then moves into Python—Pandas, NumPy, and any plotting library you prefer—for the exploratory analysis and visualisations. My biggest interest is understanding the patterns that sit beneath the numbers: what customers do, how often they return, and which factors appear to drive purchasing. Once the data story is uncovered, I would like a concise written summary and the reusable code so I can rerun the process whenever fresh data arrives. Deliverables • Well-commented SQL scripts for data extraction and cleaning • A Python notebook (or .py script) detailing the analysis, charts ...

...starts. The engine must run real-time calculations but train itself exclusively on historical sports data that I will provide (CSV format). No other feeds are necessary at this stage. The core of the job is to design, code and test a lightweight model that can ingest the historical dataset, learn patterns quickly, and then keep refining its coefficients while the game is live. Python with Pandas, NumPy, scikit-learn or a comparable ML stack is fine, as long as the final script can execute on a standard VPS and output the next “safe cash-out” multiplier through a simple CLI or REST endpoint. Please include: • Clean, well-commented source code • Brief README with setup instructions and model explanation • A short demonstration (video or screen-sha...

...Substation Automation, CT/PT sizing, and NEC code compliance. Teaching / Technical Writing Ability: Ability to explain complex electrical and software engineering concepts simply and translate them into repeatable, step-by-step algorithms. Standards: IEC 61850 (8-1 GOOSE, 9-2 SV, SCL/SCD), IEEE C37.111 (COMTRADE), IEEE 1588 PTP, ANSI protection functions (21, 50/51, 87). Software: Python 3 (numpy, pytest, lxml) or C++ (C++17/20), Linux PREEMPT_RT kernel tuning, Wireshark packet analysis. How to Apply Please submit your proposal including: Examples of technical writing, educational engineering tutorials, or P&C software projects you have created. ...

...Substation Automation, CT/PT sizing, and NEC code compliance. Teaching / Technical Writing Ability: Ability to explain complex electrical and software engineering concepts simply and translate them into repeatable, step-by-step algorithms. Standards: IEC 61850 (8-1 GOOSE, 9-2 SV, SCL/SCD), IEEE C37.111 (COMTRADE), IEEE 1588 PTP, ANSI protection functions (21, 50/51, 87). Software: Python 3 (numpy, pytest, lxml) or C++ (C++17/20), Linux PREEMPT_RT kernel tuning, Wireshark packet analysis. How to Apply Please submit your proposal including: Examples of technical writing, educational engineering tutorials, or P&C software projects you have created. Your proposed fixed-price breakdown by milestone, along with an estimated completion timeline...

...logically modular. 4. Summary report highlights why one model might outperform the other and suggests any further improvements. If you see value in optionally adding a third algorithm such as Random Forest or a Neural Network for comparison, mention it in your proposal—flexibility is welcome as long as the two core models remain the focus. Preferred stack: Python 3.x, scikit-learn, pandas, NumPy, matplotlib or seaborn....

...Substation Automation, CT/PT sizing, and NEC code compliance. Teaching / Technical Writing Ability: Ability to explain complex electrical and software engineering concepts simply and translate them into repeatable, step-by-step algorithms. Standards: IEC 61850 (8-1 GOOSE, 9-2 SV, SCL/SCD), IEEE C37.111 (COMTRADE), IEEE 1588 PTP, ANSI protection functions (21, 50/51, 87). Software: Python 3 (numpy, pytest, lxml) or C++ (C++17/20), Linux PREEMPT_RT kernel tuning, Wireshark packet analysis. How to Apply Please submit your proposal including: Examples of technical writing, educational engineering tutorials, or P&C software projects you have created. Your proposed fixed-price breakdown by milestone, along with an estimated completion timeline....

...that ingests raw financial data, cleans and normalises it, then runs machine-learning routines so the results can be consumed by dashboards, reports, or downstream models. The exact data source—stocks, crypto, or transactional feeds—is still being finalised, so the solution must stay modular enough to swap connectors without large rewrites. I expect the work to centre on Python with pandas, NumPy, scikit-learn (or similar), and a well-structured ETL workflow orchestrated by notebooks or a lightweight API layer. Good documentation and clean, reproducible code are essential; once delivered, my in-house team must be able to extend the models or plug in new data streams without your help. Deliverables • A fully functioning preprocessing and feature-engineerin...

...you see a better way Must have: Good knowledge of Analog Circuit Design and real hands-on analog circuit experience Strong grasp of AC behaviour: resonance, reactance Grounding and noise discipline, working with microvolt-level signals Good knowledge of Signal Processing Useful: Inductive sensing, coils, transformers Lock-in or phase-sensitive detection Audio-frequency electronics Python with numpy How we would work: you send instructions, we build and measure, we send back readings and photographs, you tell us what they mean and what to try next. What we provide: schematics, bill of materials, acquisition and analysis software, and components already purchased. Duration: 2 to 3 weeks, part-time....

...practices * Availability for the complete **24-hour module in person at our Mohali campus** * Willingness to participate in a short subject-screening interview ### Expected Deliverables 1. Session-wise lesson plan 2. Well-structured slides and/or notebooks 3. Practical exercises or mini-projects 4. Brief post-course assessment/feedback Python trainers should be comfortable with **Jupyter, pandas, NumPy and scikit-learn**. Relevant FinTech, RBI, PCI-DSS and ISO 27001 standards should be incorporated where applicable. Please mention **which subject(s) you can teach, your earliest availability, and your relevant teaching/industry experience**....

...data governance, CI/CD, and Snowpark-driven transformations across AWS and Azure. CORE TECHNICAL SKILLS Cloud /Dataplatforms Snowflake, dbt (Core, Cloud), SnowSQL, Snowpark, Databricks (Lakehouse), AWS, Azure ETL / Orchestration Programming BI & Applications Apache Airflow, Fivetran, Prefect, Snowpipe, Streams & Tasks Python, SQL, PySpark, Stored Procedures, UDFs, Snowpark Python, Pandas, NumPy Tableau, Streamlit (Snowflake Native Apps), Power BI DevOps & CI/CD Governance & Security Databases Git, GitLab CI/CD, GitHub Actions, Agile, Query Optimization, Performance Tuning RBAC, PII Masking, Row-Level Security, Data Quality Frameworks, Data Lineage, GDPR, CCPA MySQL, Azure SQL Server, Snowflake WORK EXPERIENCE Snowflake Architecture & RBAC Set...

...Technical Skills The ideal candidate should have strong experience in: * Python * Machine Learning / Deep Learning * Large Language Models * Natural Language Processing * Hugging Face Transformers * PyTorch * Local LLM deployment * vLLM or similar inference frameworks * Prompt engineering * Structured JSON generation * Model quantisation * Evaluation of NLP/LLM systems * Data processing with Pandas / NumPy * Git / version control Highly Desirable Experience Preference will be given to candidates with experience in one or more of the following: * Clinical NLP * Medical AI * Electronic health records * Information extraction from medical notes * Biomedical language models * Open-weight models such as Qwen, Gemma, MedGemma, Phi, or OpenBioLLM * GPU-based LLM inference * Researc...

...so we can replay historical NSE ticks and verify performance side-by-side with the live engine. Because the strategies already exist on paper, the task is purely technical: translate rules into well-structured functions, handle edge cases such as partial fills or data lags, log every decision, and expose a simple config file where I can fine-tune parameters without touching the source. Pandas, NumPy, ta-lib, and a mainstream broker SDK such as Kite or FYERS are the tools I usually see for jobs like this—use what you’re comfortable with as long as the final script behaves deterministically and is easy to audit. I’m based in Bangalore and daily collaboration is simpler if you’re nearby, but I’m open to anyone within India who can hop on quick calls d...

...Scraping, Data Extraction, Data Cleaning, Data Formatting, and Data Analysis. My requirements may include: • Web scraping and extracting data from websites • Data extraction from websites, PDFs, Excel files, and other sources • Cleaning and removing duplicate or incorrect data • Data formatting and standardization • Excel and Google Sheets data management • Data analysis using Python, Pandas, and NumPy • SQL database management and queries • Converting unstructured data into organized Excel/CSV files • Data validation and quality checking • Creating reports and dashboards when required I can provide the source website, files, or specific requirements. The final output should be accurate, clean, well-organized, and delivered in ...

...Real-time data scanning via the broker’s API I will provide • Historical data retrieval and analysis for quick back-testing • Custom alert engine able to push desktop, e-mail, or Telegram notifications I’d like the screening rules to live outside the code—JSON, YAML, a simple GUI, or a Jupyter notebook interface—so I can tweak them without touching the core logic. Feel free to lean on pandas, NumPy, ta-lib, or TensorFlow/PyTorch where appropriate, as long as setup stays straightforward on Windows. Deliverables 1. Clean, well-commented source code with 2. Step-by-step guide showing installation, API connection, and sample back-test 3. Proof of operation (screenshots or a short demo video) I will mark the job complete once I see the sca...

I have a sizeable customer-level transaction dataset covering the past two years, and I need clear, story-driven descriptive analysis ...and actionable insights. 3. An interactive dashboard (Power BI, Tableau, or a Jupyter Notebook with Plotly) so I can slice the data myself afterward. Acceptance criteria • All calculations are reproducible from the supplied code/notebook. • Visuals label axes, units and sample sizes clearly. • Commentary ties each metric back to a purchase-behavior question. Feel free to use pandas, NumPy, Matplotlib or any other analytics stack you prefer; just keep the workflow transparent. Once you deliver the final assets, I’ll run a quick spot-check on a sample of rows to ensure totals reconcile with the source files before r...

...confidence systems * Document data sources, transformations, assumptions and limitations * Work closely with the founder and development team to translate product requirements into reliable data infrastructure Technical skills we are looking for Strong experience with: * Python * SQL * PostgreSQL * APIs / REST APIs * Web scraping * ETL / ELT pipelines * Data cleaning and normalization * Pandas / NumPy * Large structured datasets * Automation * Scheduled data pipelines * Geospatial data and coordinate matching * Git / GitHub Experience with some of the following is a major advantage: * Machine learning * Recommendation engines * Ranking and scoring algorithms * Time-series data * Marine weather data * Oceanographic datasets * Environmental APIs * Meteorological data * Histori...

...from three critical quality problems—missing values, duplicate records, and flat-out incorrect fields. I need an experienced data professional to diagnose the root causes, fix the existing errors, and set up preventive checks so the same issues do not creep back in. The raw data is already exported to CSV and can be shared immediately. You are free to use the tools you know best—Python (pandas, NumPy, fuzzy-wuzzy), SQL, Power Query, Talend, OpenRefine, or even a combination—as long as the result is a clean, validated customer dataset ready for direct upload into our CRM. Deliverables • Cleaned customer data file in the original schema • A concise log or script showing every transformation step for reproducibility • Summary report that quan...

...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 performance in real time. Think Python, pandas, NumPy, scikit-learn or TensorFlow on the analytics side, married with low-latency execution and solid database design for historical tracking. If you already have a modular framework, show me how we can adapt it; otherwise outline your proposed architecture from data capture to risk reporting. This platform will be built entirely from scratch. DELIVERABLES • Research analyst: ...

...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 milestones (planning, core build, testing, deployment, and post-launch support). • An outline of deliverables for each milestone as well ...

...small pricing slips that add up. The job is to surface those slips so the finance team can see exactly where service-level fees drift from policy and how much revenue is at stake. You will start with raw exports that contain service IDs, transaction dates, listed fees, realised revenue and a few messy quirks—missing cells, outliers and, most of all, inconsistent fee records. Using Python (Pandas, NumPy) together with SQL for joins and aggregations, clean the data, quantify every charge, and spotlight the inconsistencies. Deliverables • Tidy dataset with documented cleaning steps • Jupyter notebook (reproducible, well-commented) plus separate .sql files for any queries • Statistical profile for each service: average fee, variance, revenue share and co...

I have several datasets coming in from different systems where the numeric fields are stored as plain ASCII files in some cases and UTF-8 exports in others. My goal is to run solid, reproducible data analysis on them, but first they need to be cleaned and standardised. Here is what I need from you: • Build a repeatable cleaning pipeline in Python (Pandas, NumPy) that detects the file encoding, converts everything to UTF-8 where necessary, and validates that all numeric columns are truly numeric—no stray characters, misplaced separators, or locale-specific quirks. • Where the raw files already sit in SQL Server, create T-SQL or Python-based routines that achieve the same checks and corrections in-database so I can schedule them in an Agent job. • Document...

...tuned and benchmarked. Here’s what I need from you: • Review my existing entry, exit and position-sizing rules (all documented in code and plain English). • Propose precise tweaks or entirely new modules—volatility filters, dynamic hedging, multi-leg adjustments, whatever the data justifies—and show why they improve expectancy. • Implement the changes in the same Python environment (pandas, NumPy, yfinance, Zipline-like framework) or suggest a clearly superior stack. • Back-test against at least 10 years of tick or minute data, providing clean performance metrics and equity curves. • Supply a short report that explains the rationale, parameter sensitivity and next steps for live deployment. Time is not an issue; robustness mat...

I need an experienced Full Stack AI/ML developer to build a modern, responsive AutoML web application with a professional dashboard. The application should allow users to upload CSV, Excel,...models, recommend improvements such as handling missing values, SMOTE, feature selection, and overfitting reduction, and generate downloadable PDF reports, cleaned datasets, and trained models. The project should include secure Firebase authentication, an intuitive dashboard with interactive charts, cloud deployment, and a scalable architecture using Python (Flask/FastAPI), Scikit-learn, Pandas, NumPy, and either Streamlit or React for the frontend. Please apply only if you have prior experience with AI/ML web applications and can share relevant portfolio projects, an estimated timeline, and yo...

...Developed an intelligent AI-Powered Sales Analytics Dashboard that transforms raw sales data into actionable business insights using Machine Learning and Business Intelligence techniques. The solution enables organizations to monitor sales performance, identify customer buying patterns, predict future sales trends, and make data-driven strategic decisions. The project leverages Python, Pandas, NumPy, Scikit-learn, and Power BI to clean, process, analyze, and visualize large volumes of sales data. Machine Learning algorithms are used for sales forecasting, trend analysis, customer segmentation, and predictive analytics, allowing businesses to anticipate demand, optimize inventory, and improve revenue growth. AI-driven insights help identify high-performing products, seasonal trend...

Analyzed Diwali sales data using Python (Pandas, NumPy), Excel, and Power BI to identify customer purchasing behavior and sales trends. Built an interactive Power BI dashboard to visualize revenue, top-selling products, customer demographics, state-wise sales, and category performance. Generated actionable business insights by identifying high-value customer segments and best-performing product categories, helping optimize marketing and inventory strategies.

I’m looking for a complete, end-to-end data science workflow that starts with pulling structured data from APIs and ends with a predictive model I can trust and iterate on. You’ll write clean Python code (Pandas, NumPy, Scikit-learn) that ingests the data, handles missing or inconsistent values, performs robust preprocessing, and walks through exploratory data analysis with clear, insightful visualizations using Matplotlib or Seaborn. Once the data foundation is solid, build and evaluate several machine-learning models focused on prediction, document why each algorithm was chosen, and compare their performance with the usual metrics. I value transparency, so every notebook or script must be well commented, and a short, readable methodology report should explain your dec...

Our raw sales dataset needs a full analytical pass that starts with rigorous data cleansing. The main focus is on standardising every field—dates, currencies, product codes, customer IDs—so the data can be trusted for downstream reporting. Once the formats are consistent, I want a concise exploratory analysis in Python (Pandas, NumPy, Matplotlib or Seaborn) that highlights high-level trends, seasonality, and any obvious outliers. If you spot missing values or duplicate rows along the way, feel free to resolve them; the primary acceptance criterion, however, is clean, consistently formatted data backed by a short insight summary. Deliverables • Python notebook or script that performs the cleaning and analysis • A cleaned CSV (or Parquet) file ready for f...

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