Ciencia de datos Jobs

Hola, soy Manuel Ten fundador de InyecData. Me dedico al mundo industrial y he decidido lanzar un nuevo proyecto por mi cuenta junto con un socio. Estamos desarrollando una solución que ayuda a fábricas a reducir pérdidas operativas utilizando datos y software predictivo. Ya estamos validando el problema con clientes. El proyecto está en una fase muy inicial, por lo que la idea es empezar a construir un equipo pequeño pero eficiente para desarrollar el MVP desde cero. Ahora mismo buscamos a un técnico especialista en moldes de inyección que nos asesore en la creación del software y en la parte de machine learning aplicada al proceso. Si te interesa, puedo contarte más detalles.

## Proyecto: Plataforma de IA para Predicción y Análisis de Apuestas Deportivas (Nivel Avanzado) ### Descripción general Estoy desarrollando una plataforma avanzada de análisis deportivo basada en inteligencia artificial llamada FINAL BOSS QUANT, enfocada en generar predicciones altamente precisas para apuestas deportivas (MLB, NBA, UFC, Soccer, etc.). El sistema combina múltiples modelos matemáticos y estadísticos como: - Modelos de Poisson - Simulación Monte Carlo (gran escala) - Análisis Bayesiano - Machine Learning (Auto-ML) - Value Betting (detección de valor esperado positivo) El objetivo es construir un sistema profesional, modular y altamente optimizado que pueda competir con herramientas de nivel institucion...

SOLO HISPANOHABLANTES Necesito apoyo puntual para un trabajo universitario que debo desarrollar en R Studio. Los datos con los que trabajaré son numéricos y el objetivo es extraer información clara mediante análisis descriptivo. El entregable debe incluir: • Medidas de tendencia central (media, mediana, moda). • Medidas de dispersión (varianza, desviación estándar, rango). • Distribuciones de frecuencias con tablas y gráficos básicos en ggplot2 o herramientas nativas de R. También requiero: – Código bien comentado en un script .R para que pueda reproducir cada paso. – Breve informe en PDF o Word explicando los resultados y su interpretación académica. – Ses...

Over the next three months I’m setting aside time every day for a fully hands-on, 1:1 deep dive into Data Science, Advanced Analytics, and AI. My current skill level is advanced, but I want to reinforce the foundations and then push further into Machine Learning, Deep Learning, and Data Visualization while working through real business problems. Scope • Daily live sessions (about 60–120 minutes, Monday-Friday) for 12 consecutive weeks. • A structured curriculum that begins with a quick Python + statistics refresher and moves swiftly into sophisticated modelling, MLOps, and the latest AI techniques. • Practical, code-along labs in Jupyter or VS Code after every concept—no passive slide decks. • One continuous real-time financial-modelling proje...

produce a full InSAR time-series analysis from a set of Sentinel-1 images, rather than individual interferograms. The required outputs are: A time-series deformation dataset, where displacement is tracked through time for each pixel across all acquisition dates A mean velocity map (mm/year) derived from the full time-series, showing long-term deformation rates across the study area A set of time-series graphs for selected points, illustrating how displacement changes over time In simple terms, the goal is to combine all available SAR images into a single coherent analysis that shows both the temporal evolution of ground deformation and the average deformation rate, rather than just displacement between two dates.

Heart Rate Data Analysis & Formula Development I am looking for a skilled freelancer with a background in sports physiology, biomedical engineering, or data science to support the development of formulas and algorithms derived from heart rate (HR) and R–R interval data. Scope of Work: The requirement includes deriving and validating formulas for: Heart Rate Metrics Heart Rate (HR) Resting Heart Rate (RHR) Heart Rate Variability (HRV) Parameters RMSSD SDNN pNN50 ln(RMSSD) Training Metrics Heart Rate Zones (Karvonen method) Training Load (TRIMP or equivalent) Time in Zone (TIZ) Heart Rate Recovery (HRR) Sleep & Recovery Metrics Sleep stage estimation (HR/HRV-based) Sleep score modeling Recovery/readiness score Expected Deliverables: Mathematical formulas with clear explanat...

I need a retrieval-augmented large-language-model that can (1) suggest differential diagnoses, (2) explain conditions and treatments in plain language for professionals and patients alike, and (3) surface novel insights to accelerate ongoing research projects. The scope is intentionally broad—chronic diseases, infectious diseases, and mental-health conditions must all be handled with equal rigor. The system will ingest three primary data streams: peer-reviewed medical literature, de-identified patient health records, and curated clinical-trial datasets. I already have secure access paths for each source; what I lack is the unified pipeline that cleans, embeds, and indexes the content so the model can ground every answer in verifiable evidence. Python, LangChain (or comparable orche...

Research Participant Needed: Remote Gig Workers for Academic Text Interview (Cambridge Sociology Research) Hi there, I’m Yuxia Ren, a Master’s student in Sociology at the University of Cambridge, currently conducting research on labour mobility and worker power among remote gig workers. Specifically, how you interact with the platform reputation system (e.g., star ratings, feedback scores), your labour mobility (how you find, switch, or retain gig work platforms based on your reputation), and your perceptions of worker power. This study is for my academic dissertation only (no commercial use), and I’m seeking gig workers to share their honest insights via an asynchronous text interview. Who I’m Looking For: Current remote gig workers (active on platforms like Upw...

I want to build a robust machine-learning pipeline that can reliably predict when a user is likely to log on again. The core need is a production-ready model—deep-learning is welcome where it adds value—that captures behavioural signals and translates them into accurate logon-probability scores. You will work with the raw system logs I can provide (time-stamped events, account metadata and any other fields you advise extracting). Starting from exploratory data analysis, we will move through feature engineering, model selection, hyper-parameter tuning and final evaluation. I am especially interested in interpretable insights alongside raw accuracy, so attention to explainability techniques such as SHAP or LIME is appreciated. Deliverables • Clean, well-commented Python ...

I want to turn my raw market-trends data into clear, action-ready insights with the help of a custom AI assistant. The core requirement is data analysis: the system should ingest whatever format I send—usually CSV and Excel dumps, occasionally a direct database export—and automatically clean, analyse, and visualise the material so that I can spot patterns, outliers, and emerging opportunities at a glance. I expect the workflow to be fully automated once a file is dropped into a designated folder (or a table is updated). Behind the scenes you can rely on Python, pandas, scikit-learn, or similar tooling; as long as the final solution is reproducible and easy for me to extend, the choice of libraries is up to you. I’m comfortable running scripts locally, but a lightweight w...

I need a compact, easy-to-use application that takes our historical sales data for multiple molecules and converts it into reliable demand forecasts over weekly, monthly, and quarterly horizons. Accuracy and transparency are essential because the results will drive production planning and purchasing decisions across several plants. Core scope • Ingest CSV or Excel files of historical sales pulled from our ERP. • Clean, normalize, and visualize the data so outliers or seasonality jumps are obvious. • Apply proven time-series or machine-learning techniques (ARIMA, Prophet, LSTM—whichever best fits the data volume and variability) to generate baseline forecasts and confidence intervals. • Allow me to tweak parameters—e.g., switch models, adjust seasona...

I’m building a full-featured AI sales assistant that can 1) hold natural, on-brand conversations with prospects and customers, 2) surface real-time sales analytics, and 3) automate lead capture, scoring, and routing—all in one seamless product. Core scope The software must combine an LLM-driven conversation engine with a data layer that feeds dashboards and predictive models. It should talk to every place my team already works, so native, bidirectional integrations are essential with: • CRM systems (Salesforce, HubSpot, Zoho, etc.) • Email platforms (Gmail, Outlook 365, Mailchimp, …) • Social media channels (LinkedIn, Facebook, Twitter/X) Key expectations • Modular, API-first architecture that lets me swap models or data sources without hea...

Project Brief: AI-Based Auto Trading Signal System Goal Build an AI system that automatically generates buy/sell signals for trading instruments (Gold, BTC, and others) and adapts to live market conditions for intraday trading. Core Features Needed 1. Data Ingestion Live price feeds (via APIs like Binance, Yahoo Finance, Alpha Vantage) OHLCV data (Open, High, Low, Close, Volume) News/sentiment feed (optional but powerful) 2. AI Signal Engine ML models (LSTM or XGBoost) trained on historical price data Auto-generates Buy / Sell / Hold signals No hardcoded strategy — model learns patterns from data itself 3. Adaptive Market Condition Detection Automatically detects regime: Trending / Ranging / Volatile Switches model behavior based on detected condition Re-trains or fine-tunes on recen...

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 need an AI Expert to help me in my Project, within a month. I have carried out the Analysis part of the Project. The AI person needs to recommend a relevant Model and help with 'how to do' part. This is based on a medical istrument performance analysis.

I have a sizable dataset that first needs to be cleanly extracted and then thoroughly analyzed. Because the information comes from multiple sources, I will provide a mix of Excel/CSV tables, a small hosted database dump, and several raw text exports. Your role starts with consolidating these inputs into a single, well-structured dataset, handling any duplicates or inconsistencies along the way. Once the data is tidy, I would like meaningful insights that help me understand underlying patterns. I’m especially interested in spotting emerging trends, assembling a clear, reader-friendly report, and—if the data supports it—building a simple predictive model that I can run again in the future. Please outline the statistical or machine-learning techniques you feel are most appr...

This assignment is a hands-on data assessment and evaluation. It exposes you to the various steps that data analysts go through when they receive a dataset. This is an individual assignment. Each student is expected to complete the assignment and submit their own work. Assignment Deliverable The submitted report should include a detailed discussion of the techniques you used to answer each question (see below). Include only relevant graphs and tables along with a thorough interpretation of each. Graphs and/or tables with no explanation/interpretation will not be graded. Hand in your work in a Word or PDF format. Dataset for the Assignment Download the (see attached file) It has a sample size of 70. Assignment Objective The main objective of this assignment is to develop a better under...

Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health optimization on a global scale. Join us to start Caring. Connecting. Growing together. The People Analytics team at Optum provides insights and perspective on our employees and workforce trends that allow us to fulfill our mission to help people live healthier lives and to help make the health system...

25. 1、 Title: Research on an Explainable Model of the Impact of Intracranial Artery Plaque Characteristics and Burden Based on High-Resolution Vessel Wall Imaging on Cerebral Tissue Perfusion(Need to combine information)  2、 Journal partition: SCI q2 3+OA journals I need you to match 3-5 journals based on my topic, and I may submit them based on the journals you provide me with in the future, 3、 Writing requirements: Write according to my paper title and journal requirements, and provide necessary journal requirements documents, including an AI plagiarism report Turnitin (Artificial Intelligence) (1) Word count: 5000-8000 words (2) Formulas and symbols: cannot exist in image format, nor can they be created using Word's formula editor. Please format all formulas using MathType to ensu...

Pro java & springboot developer Comfortable with AI and LLMs etc

I have a dataset containing demographic details, job performance records and results from employee-engagement surveys. Your task is to turn this raw information into a reliable attrition-prediction pipeline. Work starts with careful cleaning and preprocessing: handle missing values, encode categorical variables, standardise or normalise where needed and document every step so the workflow is fully reproducible. A brief exploratory analysis should follow to highlight key attrition drivers and verify data quality before modelling. For the classifier, I’d like you to focus on K-Nearest Neighbours. If you find that another algorithm beats KNN convincingly, feel free to present the comparison—but please include KNN in the final report. Train, tune and validate the model, then eval...

I need assistance with multiple regression analysis using JASP. The data is from a survey and involves an ordinal dependent variable with 3-5 independent variables. Requirements: - Proficiency in JASP - Experience with multiple regression analysis - Ability to interpret and present results clearly Ideal Skills: - Statistical analysis expertise - Familiarity with survey data - Strong analytical skills Please provide a sample of similar work done.

25. 1、 Title: Research on an Explainable Model of the Impact of Intracranial Artery Plaque Characteristics and Burden Based on High-Resolution Vessel Wall Imaging on Cerebral Tissue Perfusion(Need to combine information)  2、 Journal partition: SCI q2 3+OA journals I need you to match 3-5 journals based on my topic, and I may submit them based on the journals you provide me with in the future, 3、 Writing requirements: Write according to my paper title and journal requirements, and provide necessary journal requirements documents, including an AI plagiarism report Turnitin (Artificial Intelligence) (1) Word count: 5000-8000 words (2) Formulas and symbols: cannot exist in image format, nor can they be created using Word's formula editor. Please format all formulas using MathType to ensu...

I'm seeking an experienced developer to write a research paper on Leveraging Virtual Reality (VR) to Enhance Efficiency, Cost-Effectiveness, and Capability Development in U.S. Air Force Acquisition. Outline listed below Research Question/Topic (Draft – Please update). How can VR-enabled digital twins and simulators enhance efficiency, cost-effectiveness, and capability development in U.S. Air Force Acquisition? To what extent can VR-based mission scenario simulations align acquisitions decisions with operational requirements and increase mission effectiveness? Systems Development & Prototyping • Virtual Prototyping for Faster Procurement: Leveraging VR digital twins to visualize, test, and refine aircraft, weapons systems, or software before production. • VR for ...

Customer Churn Analysis & Prediction Project Overview Conducted an end-to-end Customer Churn Analysis project to identify patterns and predict customer attrition. Focused on both data exploration (EDA) and machine learning modeling to generate actionable business insights. Aimed to help businesses reduce churn, improve customer retention, and increase profitability. Dataset Details Dataset includes customer demographic, financial, and behavioral features: RowNumber, CustomerId, Surname CreditScore, Geography, Gender Age, Tenure, Balance NumOfProducts, HasCrCard, IsActiveMember EstimatedSalary Target Variable: Exited (Churn Status) Tools & Technologies Python Libraries: pandas, numpy (data processing) matplotlib, seaborn (data visualization) scikit-learn (ML modeling) Advanced Te...

I am preparing a quantitative study on “Inequality in participation versus visibility in online communities” . The raw material will come from one or more publicly-available Kaggle datasets; the challenge is to turn those data into a coherent, publication-level research project. Here is what I need from you: • Help me locate or combine the right Kaggle datasets, then document the download and preprocessing steps (Kaggle API, Python pandas, or R tidyverse are fine). • Define robust operational metrics for participation (e.g., post frequency, comment depth) and visibility (e.g., up-votes, follower counts, ranking on leaderboards). • Build the analysis pipeline—cleaning scripts, exploratory statistics, and the main inferential models (regression, GEE, or...

Hi, I am looking for a quantitative analyst or researcher who has an active Pro or Premium account on Before applying, please confirm that you already have a paid subscription and that you actively use the platform in your research or strategy development. Thank you.

Job Title: Build Bond Price Research System Using BigQuery and Machine Learning Job Description: We are seeking an experienced freelancer to develop a Bond Price Research System based on client requirements. Commbined with Machine Learning to enable powerful search, analysis, and insights into bond prices. Project Scope: - Design and build a scalable research platform for searching and analyzing bond price data (government bonds, corporate bonds, etc.). - Store and query large volumes of historical and current bond data in BigQuery. - Integrate Machine Learning models to support price analysis, trend detection, forecasting, and other research features. - Customize the solution according to the client’s specific needs. Key Requirements: 1. Technical Skills (Required): - Strong...

I have just 700 indoor photos that must be labelled for an my project upcoming object-detection model. Every visible person and every seat that is currently occupied needs its own bounding box, and because the network will train on oriented-bounding-box (OBB) data, the rectangles have to follow the exact rotation of the body or seat—especially when someone is leaning. Data is of infrared camera of a cinema check samples , please note there can be many people in one image i want each to be marked properly You will work inside the simple browser-based annotation tool I built for this purpose or any other annotation tool you like. drag the box corners, spin the angle handle where needed, hit save, and move to the next frame; the app tracks progress automatically so nothing is missed....

Do the following Stock Research Agent with SEC API and Weaviate RAG More information in the site