Hire the Best Data Scientists
Butwal, Nepal
Hi! Greeting ๐๐๐๐๐๐๐ ๐๐ ๐๐๐ ๐๐๐-๐๐๐๐๐ ๐๐๐๐ ๐๐๐๐๐๐ ๐๐๐๐ ๐๐๐!!! I am an experienced Data Scientist and Machine Learning expert with more than 12 years of experience with different companies and projects. ๐๐ฒ๐ฟ๐ฒ ๐ ๐ฎ๐บ ๐ผ๐ณ๐ณ๐ฒ๐ฟ๐ถ๐ป๐ด โ Machine Learning with Python โ Data Preprocessing with Python, R, pandas, tableau etc. โ Database management with SQL and MySQL โ Data visualization (Matplotlib, scipy, ggplot, heatmap, ipython, seaborn, Excel, Power BIetc.) โ Mathematics for Data Science, including Algebra and Statistics โ Model deployment โ Preparing report in LaTeX or Word I work on the following aspects โ Unsupervised machine learning, including K-means, PCA, HMM, etc. โ Supervised Machine learning and deep learning, including SVM, Decision Tree, Random Forest, XG Boost, NaiveBays, etc. โ Forecasting models including MA, ARMA, ARIMA, SARIMA, etc. โ Data extraction โ Decision-making based on the results of data. Let's connect for a call and explore how my seasoned expertise can work wonders for you! Regards Hari N
- LaTeX
- Data Analytics
- Machine Learning
- Mathematics
Madrid, Spain
โญ Top 1% of Data Science talent on Upwork โญTrusted by 50+ clients worldwideโญ 120+ projects and 7+ years of experienceโญClear communication, transparent pricing, and top quality. I have worked across different sectors, including: 1.1 InnoSight Financial Planning (USA): I designed an optimal portfolio based on back-projected AI-powered financial indices. 1.2 Aeuthux (USA): I led a team of data scientists to build a web platform supporting investment decision-making. 1.3 Placeholder LLC (USA): I built a trading bot using machine learning deployed on AWS to operate in cryptocurrency markets. 1.4 ARCA-X (QATAR) I wrote research papers on the financial structure of non-central banking systems.
- Data Science
- Python
- R
- Economic Analysis
- Statistical Analysis
- Time Series Analysis
- Data Analysis
- Economics
- Microeconomics
- Stata
- Econometrics
- Data Modeling
- Forecasting
- Data Science Consultation
- Statistics
Reston, Virginia
Data Solutions: Automation, Scraping, Engineering, Analysis, Visualization, and Cleanup ๐ ๐Hello! Welcome to your one-stop solution for leveraging data and streamlining business processes. Specializing in empowering businesses to achieve their operational goals, from automating tedious manual tasks to deriving insights through data analysis and visualizations, I'm here to assist in guiding you towards data-driven decision-making. Data optimization processes don't need to be costly or reliant on subscription services. With experience in supporting non-profits and small-to-medium-sized businesses, I provide cost-effective solutions tailored to your immediate needs and long-term objectives. Expertise: โ Data Automation & Integration โ Data Visualization and Dashboard Development โ Data Clean-Up โ Web Scraping & API Calls โ Custom Web Applications (for solutions, visualizations, and automations) โ Data Modeling and Architecture Proficiency: โ Python โ R Programming Language โ HTML and CSS โ Google Analytics โ Marketo โ Flask Framework โ Plotly โ Power BI โ Tableau โ Salesforce โ Jobber โ And many more! Interested in elevating your data game? I'm here to help. Reach out and letโs discuss how we can achieve your goals together.
- Data Science
- Python
- SQL
- Microsoft Excel
- Data Analysis
- Machine Learning
- pandas
- Data Scraping
- Web Application
- Data Visualization
- Business Intelligence
- Microsoft Power BI
- Tableau
- Analytics Dashboard
- Salesforce CRM
Arlington, Texas
Hi, I`m Noor ๐ "I turn raw data into clear, actionable insights that empower better decisions and drive real results, every step of the way." I'm a Data Scientist and AI Engineer. Over the past 5+ years, Iโve worked on projects that combine data engineering, machine learning, and large language models to build intelligent, production-ready solutions. I specialize in designing end-to-end ML pipelines, developing LLM-powered applications (RAG, LangChain, Llama-2/3, OpenAI), and deploying scalable systems on Azure, Databricks, and Docker. My work often involves automating data workflows, improving prediction accuracy, and transforming complex data into clear insights. Some of my favorite projects include building an AI chatbot for e-commerce, a predictive system for event planning, and an IoT protocol translator using LLMs. I value clarity, efficiency, and collaboration and I always aim to deliver results that make a measurable impact. If youโre looking for someone who can turn your data or AI idea into a working solution, Iโd be happy to help.
- MLOps
- ML Automation
- n8n
- Data Engineering
- Data Analysis
- Big Data
- Azure Machine Learning
- Databricks Platform
- Large Language Model
- Retrieval Augmented Generation
- Vector Database
- Web Development
- MEAN Stack
- MERN Stack
- React
- AI Instruction
- Technology Tutoring
- Teaching
Brahmanbaria, Bangladesh
โจ Completed Thousands of Lead List Building, Data Entry, B2B Lead Generation, Data Mining etc projects. Highly skilled and detail-oriented professional specializing in lead generation, lead list building, data entry, and data mining. With over 12 years of experience. ๐ฐ200K+ Revenue achiever ๐18000+ Hours Worked ๐ฅ300+ Jobs completed ๐ขMy goal is to support your business in reaching its targets by providing top-tier lead generation services and meticulous data entry work. Letโs connect and discuss how I can contribute to your success. โก๏ธI am adept at identifying and cultivating high-quality leads, managing extensive data sets, and extracting actionable insights to drive business growth. My dedication to accuracy and efficiency ensures that I deliver top-notch results that meet and exceed client expectations. ๐ Client satisfaction is my utmost priority. I focus on clear and timely communication, maintaining meticulous attention to detail, and employing a collaborative approach to ensure project goals are met. My ability to understand and adapt to client needs has resulted in consistent positive feedback and long-term client relationships. ๐ฅ My Best Qualities: ๐ก Lead Lists ๐ก LinkedIn Premium Account ๐ก Finding Decision Makers on Linkedin ๐ก Linkedin Prospect Building ๐ก Finding Key People from Linkedin ๐ก Lead Generation ๐ก B2B Lead Generation ๐ก Verified Valid Email Addresses ๐ก Data Mining ๐ก Data Entry ๐ก Email Sourcing ๐ก Lead Generation Specialist ๐ก Lead List Building ๐ก Lead Collect ๐ก Contact List Building ๐ก Web Research ๐ก Company Information Research ๐ก Email List Building ๐ก Contact Email and Phone Research ๐ก Prospect List ๐ก Data Extraction ๐ก Online Research ๐ก List Building ๐ก Data cleaning | Data cleansing ๐ก Sales leads | Manual data building ๐ป Tools That I use: โญLinkedIn Sales Navigator โญ Hunter โญ Apollo โญ Lusha โญ Clearbit โญ Contact Out โญ Uplead โญ Nymeriya โญ Salesql โญ Kendo โญ Snov โญSkrapp โญMailtester โญ Millionverifier โ Retrieval tools are: ๐ป Google Boolean Searches ๐ป Google Maps ๐ป CrunchBase ๐ป LinkedIn ๐ป White Pages ๐ป Yellow Pages ๐ป Yelp ๐ป DNB ๐ปManta ๐ข Why Work With Me? ๐ฅ Detail-Oriented ๐ฅ Reliability ๐ฅ Adaptability ๐ฅ Proven Track Record ๐ฅ Efficiency & Timeliness Note: Lets Discuss the project instructions and my processes
- Data Entry
- Data Mining
- Lead Generation
- Sales Lead Lists
- B2B Lead Generation
- Sales Leads
- Sales Leadership
- Data Cleaning
- Sales
- Sales & Marketing
- Contact List
- Email List
- LinkedIn Sales Navigator
- Prospect List
- Email Sourcing
- LinkedIn Lead Generation
- List Building
- Data Collection
- Data Processing
- Contact Info Research
Lahore, Pakistan
I have spent 8 years at the intersection of data, AI, and the question nobody wants to ask โdoes it actually deliver results?โ From forecasting systems to LLM pipelines and autonomous agents built for real world problems where off-the-shelf solutions fail. The tools change with every project. The bar doesn't. Here is an overview of my Stack ๐ ๐ ๐๐ฟ๐ฎ๐บ๐ฒ๐๐ผ๐ฟ๐ธ๐: PyTorch, TensorFlow, Scikit-learn, XGBoost, LightGBM, CatBoost, statsmodels ๐๐๐ ๐ & ๐ก๐๐ฃ: Open AI, Claude, Gemini, Grok, LLaMA, Mistral, DeepSeek, BERT, BART, SetFit, HuggingFace ๐๐ด๐ฒ๐ป๐๐ถ๐ฐ & ๐๐๐๐ผ๐บ๐ฎ๐๐ถ๐ผ๐ป: LangChain, LangGraph, RAG Pipelines, n8n, Make, OpenAI API, Anthropic API, Lovable, OpenClaw ๐ฉ๐ฒ๐ฐ๐๐ผ๐ฟ & ๐ฆ๐ฒ๐ฎ๐ฟ๐ฐ๐ต: Pinecone, FAISS, ChromaDB, SentenceTransformers, Embeddings ๐๐ฎ๐๐ฎ ๐๐ป๐ด๐ถ๐ป๐ฒ๐ฒ๐ฟ๐ถ๐ป๐ด: pandas, NumPy, Parquet, Airflow, dbt, ETL Pipelines ๐๐ฃ๐๐ & ๐ฆ๐ฐ๐ฟ๐ฎ๐ฝ๐ถ๐ป๐ด: FastAPI, Flask, WebSocket, PRAW, BeautifulSoup, Selenium ๐ฉ๐ถ๐๐๐ฎ๐น๐ถ๐๐ฎ๐๐ถ๐ผ๐ป: Matplotlib, Seaborn, Plotly, Tableau, PowerBI, SHAP ๐๐น๐ผ๐๐ฑ & ๐๐ป๐ณ๐ฟ๐ฎ: AWS EC2, SageMaker, AWS Bedrock, Firebase, Docker, VPS ๐๐ฟ๐ผ๐ป๐๐ฒ๐ป๐ฑ & ๐๐ฝ๐ฝ๐: React, Next.js, Streamlit, Gradio, Lovable ๐๐ป๐๐ฒ๐ด๐ฟ๐ฎ๐๐ถ๐ผ๐ป๐: Gmail API, Google Calendar API, WhatsApp API, Stripe, PayPal, Odoo You can get a feel for the work pretty quickly. Here's a slice. โ ๐๐ ๐๐๐๐ผ๐บ๐ฎ๐๐ถ๐ผ๐ป & ๐๐ด๐ฒ๐ป๐๐ถ๐ฐ ๐ฆ๐๐๐๐ฒ๐บ๐ โข Built a ๐๐๐๐-๐๐๐๐๐ ๐จ๐ฐ ๐๐๐๐๐๐ ๐๐๐๐๐๐๐๐ using n8n to orchestrate OpenAI-powered resume parsing with Gmail, Google Sheets, and Calendar APIs reducing ๐ป๐ ๐๐๐๐ข๐๐ ๐ค๐๐๐๐๐๐๐ ๐๐ฆ 80% with centralized candidate tracking and automated scheduling. โข Developed ๐ ๐๐๐๐-๐๐๐๐ ๐จ๐ฐ ๐๐๐๐๐ ๐๐๐๐๐ supporting voice-to-voice, speech-to-text and text-to-text conversations via FastAPI and WebSocket with ultra low latency using GPT for dialogue management. โข Built an ๐จ๐ฐ ๐๐๐๐๐๐๐ ๐๐๐๐๐๐๐ ๐๐๐๐๐ ๐๐๐๐๐๐๐๐ on Next.js and Firebase with role-based AI prompts, automated symptom collection and ๐๐๐๐ ๐ก๐๐๐ ๐๐๐๐๐๐๐๐ ๐๐๐ ๐๐โ๐ก๐ for patient doctor interaction. โ ๐๐ผ๐ฟ๐ฒ๐ฐ๐ฎ๐๐๐ถ๐ป๐ด & ๐ฃ๐ฟ๐ฒ๐ฑ๐ถ๐ฐ๐๐ถ๐๐ฒ ๐ ๐ผ๐ฑ๐ฒ๐น๐ถ๐ป๐ด From pharmaceutical supply chains to crypto markets, I build forecasting systems that drive real inventory, budget and trading decisions. โข Built a 3๐ด+ ๐๐๐๐๐๐ ๐๐๐๐๐๐ ๐๐๐๐๐๐๐๐๐๐๐ ๐๐๐๐๐๐ pipeline: XGBoost Rยฒ=0.90, 20% accuracy gain, 17-chart EDA uncovering SKU concentration risk and billing-cycle demand patterns โข ๐ช๐๐๐๐๐๐๐ ๐๐๐๐๐๐๐๐๐๐๐ ๐๐๐ ๐๐๐ using ARIMA + Reddit sentiment (PRAW + SetFit) โ BUY/SELL/HOLD signals for BTC, ETH, SOL, DOGE โข ๐ซ๐๐๐๐๐ ๐๐๐๐๐๐๐๐๐๐๐ ๐๐๐๐๐๐๐๐ (LR, XGBoost, RF, LSTM) achieving Rยฒ~0.99 used car price prediction deployed via Flask โ ๐ ๐ฎ๐ฐ๐ต๐ถ๐ป๐ฒ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด & ๐ฆ๐๐ฎ๐๐ถ๐๐๐ถ๐ฐ๐ฎ๐น ๐ ๐ผ๐ฑ๐ฒ๐น๐ถ๐ป๐ด I build classification, regression, and validation systems with rigorous evaluation not just accuracy scores but defensible, ๐๐๐๐ ๐๐๐๐๐๐-๐๐๐๐ ๐ ๐๐๐ ๐๐๐. โข SVM, Gradient Boosting, MLP, XGBoost, Logistic Regression always with GridSearch and KFold CV for hyperparameter integrity โข Diabetes detection: 86% accuracy on 3-class imbalanced clinical dataset with feature engineering and undersampling experiments โ ๐ก๐๐ฃ & ๐๐๐ -๐ฃ๐ผ๐๐ฒ๐ฟ๐ฒ๐ฑ ๐๐ฎ๐๐ฎ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐ฐ๐ฒ I combine classical text modeling with modern LLMs to extract structured insight from unstructured data at scale. โข Claude 3.5 Sonnet (AWS Bedrock) + BART MNLI + SentenceTransformer pipeline quantifying open ended survey sentiment for fragrance product strategy โข Real-time Reddit ๐๐๐๐๐๐๐๐๐ ๐ ๐๐๐๐๐๐๐๐ for ASTS ticker upvote-weighted transformer scoring with daily trend visualization โข ๐ป๐๐๐ ๐ช๐๐๐๐๐๐๐๐๐ across disaster tweets (TFIDF, 80%), IMDB reviews (LSTM, 86%) and news categorization (CNN + GloVe, 75%) โข GPT-4o, Claude, LLaMA, Grok and Mistral used as deliberate data enrichment and annotation tools inside ML pipelines I work with startups building their first AI product, enterprises with complex data problems, and individuals with unique challenges nobody else wants to touch. If the problem is hard and the data is messy that's exactly where I do my best work. Send me a message and let's figure out if I'm the right fit. I will tell you within 24 hours whether I can help and how.
- Data Science
- Python
- Natural Language Processing
- Deep Learning
- Machine Learning
- Data Scraping
- Data Visualization
- Data Analysis
- Chatbot Development
- LLM Prompt Engineering
- Artificial Intelligence
- AI Chatbot
- AI Agent Development
- Deep Learning Modeling
- PyTorch
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Resources to help you hire

Cost to hire a Data Scientist
Explore typical Data Scientist rates and what businesses pay to hire top talent.

Data Scientist job description template
Get tips to write a job post that attracts qualified Data Scientists.

Data Scientist interview questions
Top interview questions to help you hire the right Data Scientists, faster.
Resources to help you hire

Cost to hire a Data Scientist
Explore typical Data Scientist rates and what businesses pay to hire top talent.

Data Scientist job description template
Get tips to write a job post that attracts qualified Data Scientists.

Data Scientist interview questions
Top interview questions to help you hire the right Data Scientists, faster.
Data scientist hiring guide
Data scientists turn raw data into strategic insights that drive business decisions. Whether you need to build predictive models, optimize operations, or uncover customer patterns, hiring the right data scientist can transform how your organization uses data.
What does a data scientist do?
A data scientist analyzes complex datasets to extract actionable insights that drive business decisions. They combine statistical expertise, programming skills, and domain knowledge to turn raw data into strategic advantages.
Key responsibilities include:
Data collection and preparation. Gathering data from multiple sources such as internal databases, third-party APIs, and web scraping. They spend significant time cleaning datasets to prevent garbage-in, garbage-out scenarios.
Exploratory analysis. Using statistical methods to identify patterns, trends, and relationships in data.
Predictive modeling. Building machine learning models that forecast outcomes like customer behavior, sales trends, or operational risks.
Machine learning deployment. Developing and deploying algorithms for tasks like recommendation systems, fraud detection, or process automation.
Data visualization. Creating dashboards and reports that make insights accessible to executives, using tools like Tableau, Power BI, or Matplotlib.
Experimentation. Designing and analyzing A/B tests to validate hypotheses and guide product decisions.
How to hire a data scientist on Upwork
Upwork makes it easy to find and hire freelance data scientists, with many skilled candidates available to meet your timeline and budget needs. To streamline your hiring process, just follow these four simple steps.
Step 1: Craft a targeted job post
A well-crafted job post attracts data scientists with the specific expertise your project requires. In your post:
Describe your business problem and expected deliverables (i.e., building predictive models or dashboards, boosting sales or reducing costs)
List required technical skills like Python, SQL, or TensorFlow
Give a realistic range for required experience relative to your budget
To create a tailored job post quickly, try the Job Post Generator powered by Umaโข, Upworkโs Mindful AI. Describe what you need in a few sentences, and Uma will craft a job post in seconds. You can also review data scientist job description templates for ideas and inspiration.
Step 2: Filter and evaluate proposals
Taking a structured approach to reviewing proposals will help you move efficiently from a large applicant pool to a focused shortlist.
Have Uma give instant video interviews and side-by-side comparisons
Use Upworkโs filters to find candidates by rate, location, and experience
Review proposals for signs that the candidate has understood your job post and has the skills to meet your needs
Review portfolios for past projects and case studies that show measurable results
Step 3: Interview your top choices
Quick video interviews give you the chance to ask any questions you have left for your top candidates, and to get a feel for what a collaboration with them might be like.
Schedule and conduct interviews within Upwork messaging to get instant transcripts and summaries from Uma
Ask the candidates to walk you through past work from their portfolio, focusing on aspects that are similar to your project and challenges they overcame
Discuss their process for data collection and cleaning, and other processes relevant to your project
Have them walk you through what overfitting might look like, and how they handle missing data in a dataset
Cover key soft skills, such as how they present complex topics to non-technical stakeholders
To help you prepare for the interviews, especially if you arenโt technically minded, consider reviewing data scientist interview questions.
Step 4: Agree on scope and begin work
Once youโve found the right person, you can send a contract directly through the Upwork marketplace. A solid contract protects both parties and helps collaborations be successful from beginning to end.
Use Upwork's contract workroom, messaging, and payment protection for secure collaboration
Choose fixed-price contracts for projects with clear deliverables, such as a single dataset analysis and summary
Break large projects into milestones, such as data collection, cleaning and processing, ML model training, model validation, and deployment
Choose hourly contracts for ongoing work or projects without clear deliverables, such as ML model monitoring, retraining, and fine tuning
Upwork is not affiliated with and does not sponsor or endorse any of the tools or services discussed in this article. These tools and services are provided only as potential options, and each reader and company should take the time needed to adequately analyze and determine the tools or services that would best fit their specific needs and situation.
The rates and information provided in this article are based on current data and industry sources available at the time of publication. Freelance rates can vary depending on factors such as experience, location, project scope, and market conditions. Readers are encouraged to conduct their own research to confirm current rates and trends, as this information may change over time.
How much does hiring a data scientist cost?
Independent data scientists on Upwork charge prices ranging from $35-$250 per hour. Your exact cost will depend on the scope and complexity of the project, as well as the skills and experience of the professional. The following chart lists typical costs for data science projects often found through Upwork.
Data analysis and reporting
$1,500-$5,000 /project
- Single dataset analysis and statistical summary
- Basic visualizations
- Insights report with recommendations
Predictive model development
$5,000-$15,000 /project
- Custom ML model design and training
- Validation and accuracy testing
- Deployment guide
End-to-end data science solution
$15,000+ /project
- Complete data pipeline setup
- Multiple model development
- System integration and training
Ongoing analytics support
$4,000-$15,000 /month
- Monthly KPI dashboards
- Model performance monitoring
- Ad hoc analysis and improvement
Strategic data science consulting
$10,000-$30,000+ /project
- Data maturity assessment
- ML roadmap development
- Team capability building
FAQs about data scientists
Frequently asked questions
Is hiring a data scientist worth it?
Hiring a data scientist is worth it when you have meaningful data and business questions requiring specialized analysis. They can optimize pricing strategies, predict customer churn, identify operational inefficiencies, and uncover revenue opportunities that would otherwise remain hidden.
What skills should I look for when hiring a data scientist?
Essential technical skills include proficiency in programming languages (Python, R, SQL), statistical analysis, machine learning frameworks (TensorFlow, scikit-learn, PyTorch), and data visualization tools (Tableau, Power BI).
Beyond technical abilities, look for strong problem-solving skills, business acumen, and clear communication to explain findings to non-technical stakeholders.
What is the difference between a data analyst and a data scientist?
A data analyst focuses on descriptive work โ understanding what happened through reports and dashboards. A data scientist builds predictive machine learning models to forecast what will happen and recommend actions. If historical analysis fits your needs, consider hiring a data analyst. Read more about comparing the two roles.
What's the difference between a data scientist and a machine learning engineer?
A data scientist explores data and builds prototype models. A machine learning engineer deploys those models into production applications at scale, focusing on software engineering and system infrastructure. If you need production deployment, consider hiring a machine learning engineer. Read more to compare the two roles.
How can a data scientist add value to my business?
A data scientist adds value by solving specific business problems with data-driven approaches. Common value-adds include increasing revenue through recommendation systems, reducing costs with predictive maintenance, and improving customer experience through segmentation.
How do I evaluate a data scientist's work quality?
For technical quality, review model performance metrics (accuracy, precision, recall), assess methodology documentation, and verify code reproducibility. For business impact, determine if findings are actionable and assess how clearly they communicate results. On Upwork, set project milestones to review work incrementally.
Find more freelancers
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