Sergei is currently unavailable
Sergei Markochev
Verified Expert in Engineering
Data Scientist and Developer
London, United Kingdom
Toptal member since May 24, 2021
Bio
Sergei is a lead data science and AI/ML developer with extensive experience—over 15 years' worth. He has led end-to-end project delivery and provided technical expertise for complex decision problems for FTSE 100 companies and SME businesses. Sergei possesses a PhD in Physics, has one patent and six academic papers, and recently won 1st place in an international data science competition.
Portfolio
Revolution Dancewear, LLC
Data Science, Data Analysis, Data Analytics, Inventory Management...
Status Pros, LLC
Machine Learning, Data Science, Artificial Intelligence (AI), Python...
Docme Technologies Ltd
Microsoft Azure, Azure, Data Science, Data Engineering, Risk Models...
Experience
- Applied Mathematics - 14 years
- Data Analysis - 14 years
- Software Development - 10 years
- Machine Learning - 10 years
- Data Science - 10 years
- Nonlinear Optimization - 6 years
- Artificial Intelligence (AI) - 4 years
- Deep Learning - 4 years
Preferred Environment
Jupyter Notebook, Windows, Linux, Git, Python, Amazon Web Services (AWS), Visual Studio Code (VS Code)
The most amazing...
...algorithm I've developed was ranked number one at an aircraft localization data science competition hosted by AIcrowd.
Work Experience
Senior Data Analyst
2026 - 2026
Revolution Dancewear, LLC
- Conducted transactions data analysis to reveal sales trends for product categories.
- Performed churn analysis and analyzed the behavior of new and returning customers using transactions data.
- Investigated the most ordered items and categories and their combinations from transaction data.
Technologies: Data Science, Data Analysis, Data Analytics, Inventory Management, Multivariate Statistical Modeling
Senior Data Scientist/AI Expert
2025 - 2026
Status Pros, LLC
- Built a prototype for the client to chat with data stored in SharePoint, including PDFs, presentations, and other documents.
- Developed an indexing system to catalog documents stored in SharePoint.
- Developed an automation pipeline to process new documents uploaded to SharePoint and search over them.
Technologies: Machine Learning, Data Science, Artificial Intelligence (AI), Python, Automation, AI Agents, AI Automation, AI Consulting, Consulting, Robotic Process Automation (RPA), RAG Systems, Decision Modeling, Microsoft Copilot, Embedding Models, ETL Development, Vector Databases, ETL for AI, Document Processing, REST APIs, Workflow Automation & System Integration
Data Scientist/Data Engineer
2025 - 2026
Docme Technologies Ltd
- Downloaded publicly available laboratory test data and built an ETL to combine them.
- Evaluated the approach and methodology described in a Nature article and proposed improvements from the data science perspective.
- Developed a risk predictive model to estimate a person's BioAge using blood biomarkers.
Technologies: Microsoft Azure, Azure, Data Science, Data Engineering, Risk Models, Risk Modeling, Statistics, Health, Healthcare, Predictive Modeling, scoring, Data Modeling, Research
Data Scientist | Business Analyst
2025 - 2025
The Business of Fashion Limited
- Carried out an analysis of web traffic and the conversion funnel of a large website.
- Investigated how the frequency and quality of published materials affect the conversion funnel.
- Used multivariate regression to identify critical indicators of the conversion funnel that need to be improved.
Technologies: Statistical Modeling, Feature Engineering, Data Science, Correlational Analysis, Churn Analysis, Funnel Analysis, Statistical Analysis, Looker, Snowflake, Traffic Analysis, Data Build Tool (dbt), Monthly Recurring Revenue (MRR), ARPU, SAS, Pricing, Visual Studio Code (VS Code), Time Series, Google BigQuery, Predictive Modeling, Multivariate Statistical Modeling, Data Integration
Data Scientist (Python)
2025 - 2025
Media Analytics LTD
- Reproduced results from the paper published in the Nature journal regarding extreme flood and drought events.
- Preprocessed and analysed scientific data from the GRACE-FO project to calculate water storage changes on Earth in the past 20 years.
- Improved the spatio-temporal clusterization algorithm and carried out water anomaly clusterization.
Technologies: Python, Data Science, Data Visualization, Data Analysis, Simulators, Clustering, Climate Change, Visual Studio Code (VS Code), Predictive Modeling, Classification, GIS, Research
Pricing Data Scientist
2025 - 2025
Insight Factory, LLC
- Analyzed sales funnel datasets and proposed models to predict target variables.
- Carried out a basket analysis of the provided dataset and found association rules.
- Studied the pricing dataset and proposed an effective pricing strategy for the business.
Technologies: Data Science, Python, Data Analytics, Pricing Strategy, Pricing Models, Dynamic Pricing, Data Pipelines, Financial Modeling, Competitor Analysis & Profiling, Competitive Strategy, Google Cloud Platform (GCP), Databricks, eCommerce, Retail & Wholesale, Funnel Analysis, Business Intelligence (BI), Visual Studio Code (VS Code), Google BigQuery, Inventory Management, Statistics, Multivariate Statistical Modeling, Pricing
Prompt Engineer
2024 - 2025
2x Solutions, LLC
- Developed prompts to improve the identification of financial compliance issues.
- Fine-tuned ChatGPT models to accurately classify the outcome of voice AI calls.
- Prepared a large training dataset of voice AI call outcomes and identified edge cases.
Technologies: Artificial Intelligence (AI), Prompt Engineering, Machine Learning, OpenAI, Fine-tuning, ChatGPT, API Integration, Visual Studio Code (VS Code), Agentic AI, AI Automation, AI Consulting, scoring, Classification, ETL Development, Model Evaluation, ETL for AI, Document Processing
Data Scientist
2024 - 2025
Rados Administration Services Limited
- Developed an algorithm to automatically identify the position of rock samples on a tray and match them with chemical records.
- Carried out calibration of rock samples using data from the XRF sensor.
- Developed a Dash-based web dashboard connected to a database, where sample calibration, investigation, etc. can be performed.
Technologies: Data Science, Python, SQL, Machine Learning, Matplotlib, Microsoft SQL Server, Statistical Methods, Scikit-learn, MySQL, Pandas, NumPy, Dash, Web Dashboards, Nuclear Physics, Spectroscopy, Mining, Digital Signal Processing, Visual Studio Code (VS Code), Classification, Model Evaluation
Data Engineer
2024 - 2025
Carbonfuture
- Developed a data model for the company to connect buyers and suppliers.
- Implemented a data integration solution between the HubSpot API and the data warehouse.
- Built a Microsoft Power BI dashboard for the company's business analytics function.
Technologies: ETL, SQL, HubSpot API, Microsoft Power BI, APIs, Reporting, Data Engineering, PostgreSQL, Data Architecture, Data Build Tool (dbt), Analytics, Climate Change, Business Intelligence (BI), HubSpot, API Integration, Visual Studio Code (VS Code), Microsoft Azure, Inventory Management, Cloud Platforms, Data Warehousing, ETL Development, Data Integration, SQL, REST APIs
Data Scientist | Data Analyst
2024 - 2025
Ultraspeed Digital Limited
- Developed software and algorithms to simulate sensors' response to a person's footsteps.
- Conducted research and carried out mathematical modeling of sprint athletes' kinematics.
- Performed experiments with sensor equipment and preprocessed and analyzed data.
Technologies: Data Science, Data Analysis, Python, SQL, Microsoft Power BI, ETL, Data Analytics, Modeling, Applied Mathematics, Applied Physics, Analytics, Financial Modeling, Simulations, Visual Studio Code (VS Code), Data Modeling, Research
Data Scientist
2022 - 2024
12435136 Canada Inc.
- Developed a revenue management system for predicting optimal prices and revenue for some real estate properties.
- Built a pricing engine for predicting real estate properties' market price and elasticity.
- Created data processing pipelines to increase data quality.
- Implemented a chat interface using the ChatGPT model to allow users to retrieve modeling results.
Technologies: Data Science, Predictive Modeling, Pricing Models, SQL, Python, Dynamic Pricing, Forecasting, Machine Learning, ChatGPT, AI Agents, Large Language Models (LLMs), AWS Serverless Application Model (SAM), Amazon Web Services (AWS), Software Development, Monte Carlo Simulations, Mathematical Modeling, GitLab CI/CD, Data Preprocessing, Applied Mathematics, Revenue Modeling, LangChain, Transformer Models, Docker, FastAPI, AWS Lambda, Random Number Generation, AI Chatbots, OpenAI GPT-3 API, OpenAI GPT-4 API, AI Model Training, OpenAI, Prompt Engineering, Conversational AI, APIs, Data Architecture, Data Engineering, Reporting, Analytics, Unstructured Data Analysis, Artificial Intelligence (AI), Fine-tuning, OpenAI API, Chatbots, Data Pipelines, Financial Modeling, Data Classification, Feature Engineering, Back-end, Simulations, Monte Carlo, AI Prompts, CrewAI, Predictive Analytics, Gradient Boosting, Time Series Data, Revenue Management, Pricing, Visual Studio Code (VS Code), Agentic AI, ETL Pipelines, ETL Tools, Beautiful Soup, Beautiful Soup 4, Web Scraping, AI Consulting, TensorFlow, Decision Modeling, Statistics, scoring, GIS, Multivariate Statistical Modeling, Cloud Platforms, Data Warehousing, ETL Development, Model Evaluation, Data Modeling, GeoPandas, Geographic Information Systems, Data Integration, Data Cleansing, Google Maps API, Property Valuation, Pricing, Pricing Models, SQL, REST APIs
Data Science and Analytics Manager
2022 - 2024
CI&T
- Developed a personalized upselling suggestion recommendation system for one of the top five worldwide fast food chain's mobile apps.
- Deployed the new recommendation system for upsells into the production environment using Terraform and AWS services.
- Planned and performed A/B testing of the new recommendation system for upsells, which showed over 50% improvement.
- Led data analytics and data quality projects for CI&T international clients.
- Championed data science in CI&T UK and developed POC using generative AI models.
Technologies: Amplitude, Jupyter Notebook, Snowflake, Data Analytics, A/B Testing, SQL, Jira, Management, AWS Serverless Application Model (SAM), Recommendation Systems, AWS Step Functions, Amazon Web Services (AWS), Big Data, Data Scraping, Terraform, Python, Time Series, CI/CD Pipelines, Data Quality Analysis, ARIMA, Algorithms, Solution Architecture, XGBoost, Machine Learning Operations (MLOps), Docker, Transformer Models, FastAPI, AWS Lambda, Collaborative Filtering, AWS IoT, Data-informed Recommendations, Amazon SageMaker, Random Number Generation, OpenAI GPT-3 API, OpenAI GPT-4 API, AI Model Training, OpenAI, Prompt Engineering, APIs, Data Architecture, Data Engineering, Reporting, Analytics, Unstructured Data Analysis, Artificial Intelligence (AI), Computer Vision, Data Pipelines, eCommerce, Retail & Wholesale, Data Classification, Feature Engineering, Statistical Analysis, Correlational Analysis, Data Build Tool (dbt), Back-end, Business Intelligence (BI), AI Prompts, API Integration, Gradient Boosting, Time Series Data, Visual Studio Code (VS Code), TensorFlow, mParticle, LaunchDarkly, ETL Pipelines, AI Consulting, Consulting, Statistics, Predictive Modeling, scoring, Data Warehousing, Embedding Models, ETL Development, Model Evaluation, Data Cleansing, SQL
Software Developer
2021 - 2023
Tellusant
- Improved data quality and filled data gaps using machine learning and custom modeling.
- Reviewed code and helped to build an MVP. Implemented time-series prediction.
- Investigated opportunities to predict audiences for specific products through analysis of global data.
Technologies: Python, Mathematics, Algorithms, Azure, PostgreSQL, ARIMA, Time Series, Time Series Analysis, Analytics, Simulations, Predictive Analytics, Time Series Data, Visual Studio Code (VS Code), ETL Development, Data Modeling, Data Integration
Senior Data Scientist
2022 - 2022
Kainos
- Developed a data-informed recommendation system for extraction, manipulation, and search of helpful information from employees' resumes. Used natural language processing (NLP) techniques.
- Led data investigation and prototype model development for the client (a construction company).
- Presented some advanced topics on application deployment on AWS for an internal deep dive session.
Technologies: Python, Python 3, Azure, Azure SQL, Jupyter Notebook, Machine Learning, Statistics, Dash, Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), Software, SpaCy, Amazon Web Services (AWS), Data Scraping, Recommendation Systems, Data-informed Recommendations, Document Parsing, Unstructured Data Analysis, Artificial Intelligence (AI), Computer Vision, Data Classification, Feature Engineering, Visual Studio Code (VS Code), Microsoft Azure, Consulting, Classification, Embedding Models, Model Evaluation
ML/AI Developer
2021 - 2022
Colin Williams
- Scraped publicly available data from a government website using BeautifulSoup.
- Cleaned and analyzed text data following Colin's suggestions.
- Built a predictive model (classifier) to prove Colin's theory.
Technologies: Machine Learning, Predictive Modeling, Artificial Intelligence (AI), Beautiful Soup, Beautiful Soup 4, Web Scraping, Website Data Scraping, Decision Modeling
Machine Learning (ML) Engineer
2021 - 2022
Bowen & Associates Ltd.
- Developed a state-of-the-art ML model to predict commercial property prices.
- Deployed the ML model on AWS to test its predictions.
- Advised the client on advances and limitations of the model, data quality, and deployment for testing.
Technologies: Machine Learning, Classification Algorithms, Regression Modeling, AI Model Training, APIs, Reporting, Analytics, Artificial Intelligence (AI), Google Cloud Platform (GCP), Feature Engineering, Back-end, Predictive Analytics, Visual Studio Code (VS Code), ETL Pipelines, ETL Tools, GIS, Multivariate Statistical Modeling, ETL Development, Model Evaluation, GeoPandas, Geographic Information Systems, Data Cleansing, Google Maps API, Property Valuation, Pricing, Pricing Models
Lead Data Scientist
2018 - 2022
GroupM
- Productionized three apps related to investigating and optimizing global TV ad schedules.
- Developed a cross-media data fusion model with an external deduplication data set.
- Predicted digital behavior for target audiences defined by TV show viewership and vice versa using ML techniques.
- Created Looker dashboards to present POCs and data insights.
- Developed deep learning models of reach curves for individual TV channels and other combinations.
- Carried out a bespoke analysis for multibillion-dollar stakeholders.
- Communicated results to stakeholders and product managers. Managed and hired data scientists.
Technologies: Machine Learning, Data Analysis, SQL, Data Cleaning, Cython, Software Development, Agile, R, Looker, Deep Learning, Nonlinear Optimization, Clustering, Git, Pandas, Keras, Artificial Intelligence (AI), Data Science, Quantitative Research, Amazon Web Services (AWS), Data Visualization, ETL, MySQL, Python, Data Analytics, Scikit-learn, Dashboard Development, Time Series, NumPy, SciPy, Jupyter Notebook, Statistics, Unsupervised Learning, Big Data, XGBoost, AWS Lambda, Collaborative Filtering, Random Number Generation, AI Model Training, Data Engineering, Reporting, Analytics, Fine-tuning, Data Pipelines, Google Cloud Platform (GCP), Data Classification, Feature Engineering, Statistical Analysis, Correlational Analysis, Looker Studio, Looker Modeling Language (LookML), Business Intelligence (BI), Gradient Boosting, LightGBM, TensorFlow, Principal Component Analysis (PCA), ETL Pipelines, ETL Tools, Predictive Modeling, Classification, Cloud Platforms, Data Warehousing, ETL Development, Model Evaluation, Data Integration, Research, Data Cleansing, SQL
Data Scientist (Python)
2021 - 2021
Applied AI LLC
- Developed an ML model to classify the content of industry-specific PDF documents.
- Investigated different approaches (ML, NLP, and statistical) to the modeling of document content.
- Assisted the client on best practices and models during the project.
Technologies: Python, PDF Scraping, Data Science, Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), Data Analytics, Machine Learning, Deep Learning, Transformer Models, Document Parsing, AI Model Training, Unstructured Data Analysis, Artificial Intelligence (AI), Computer Vision, Fine-tuning, Data Classification, Feature Engineering, Classification, Model Evaluation, Document Processing
Battery Analytics Scientist
2015 - 2018
BBOXX LTD
- Invented and deployed a patented state-of-the-art algorithm for remote capacity estimation of lead-acid batteries by their telemetry.
- Produced insights on battery performance and customer usage patterns to reduce battery failure maintenance.
- Developed advanced alerting and anomaly detection systems to monitor over 100,000 solar panels’ performance (broken sensors, tampering, heavy usage, and so on).
- Developed a Bayesian survival model for the prediction of battery failure rate in the future.
Technologies: Digital Signal Processing, Data Analysis, Machine Learning, Nonlinear Optimization, Data Cleaning, Cython, Software Development, Agile, Linux, Monte Carlo Simulations, SQL, Bayesian Inference & Modeling, Clustering, Git, Pandas, Object-oriented Programming (OOP), Mathematics, Data Science, Amazon Web Services (AWS), Data Visualization, MySQL, Python, Data Analytics, Scikit-learn, Dashboard Development, Time Series, NumPy, SciPy, PostgreSQL, Jupyter Notebook, Unsupervised Learning, Big Data, Random Number Generation, Analytics, Artificial Intelligence (AI), Churn Analysis, Statistical Analysis, Correlational Analysis, Back-end, Simulations, Monte Carlo, Localization, Predictive Analytics, Gradient Boosting, LightGBM, Sensor Data, Time Series Data, Anomaly Detection, Principal Component Analysis (PCA), Risk Models, Risk Modeling, ETL Tools, Statistics, Predictive Modeling, scoring, Multivariate Statistical Modeling, Data Warehousing, ETL Development, Model Evaluation, Geographic Information Systems, Research, Data Cleansing, SQL
Assistant
2009 - 2014
Moscow Institute of Physics and Technology
- Supported and organized the educational process, conducted courses, and supervised bachelor degree routes.
- Organized and provided the department’s section at the annual university conference.
- Led laboratory courses and seminars on atomic physics and optics.
Technologies: University Teaching, LaTeX, Applied Physics
Senior Research Associate
2007 - 2014
Central Institute of Chemistry and Mechanics
- Led the experimental research on rare nuclear decays (published in five academic papers and reported on in four international conferences).
- Developed a fully automated digital spectroscopic system for the investigation of rare nuclear decays (Ph.D. thesis).
- Carried out data analyses and Monte Carlo simulations.
Technologies: Data Analysis, Monte Carlo Simulations, University Teaching, C++, Digital Signal Processing, Software Development, Data Cleaning, Applied Mathematics, Applied Physics, MATLAB, Object-oriented Programming (OOP), Mathematics, Statistics, Statistical Analysis, Simulations, Monte Carlo
Experience
Aircraft Localization Competition
https://github.com/smarkochev/Aircraft_localization_competition_round_2In this competition, participants determine the aircraft positions based on time of arrival and signal strength measurements reported by many low-cost crowdsourced sensors. Only some receivers provide GPS-synchronized timestamps, while others experience strong clock drifts or provide fully broken timestamps. The competition was organized by the Swiss Cyber-Defence Campus of Armasuisse Science and Technology. The data was collected by the OpenSky Network, a large-scale ADS-B sensor network for research.
• https://www.aicrowd.com/challenges/cyd-campus-aircraft-localization-competition/leaderboards
• https://www.aicrowd.com/challenges/cyd-campus-aircraft-localization-competition/leaderboards
Prediction of Customer Spending
https://github.com/smarkochev/ds_notebooks/A data analysis of customer purchase history and prediction on their total spending in the future using Bayesian modelling and Monte Carlo simulation.
Notebook:
• Prediction of customer spending.ipynb
Notebook:
• Prediction of customer spending.ipynb
Expedia Hotel Sales | Kaggle Competition
https://www.kaggle.com/c/hotelsales/A Kaggle indoor competition aimed at predicting hotel sales for the first 10 days for a subset of new Expedia hotels (for which Expedia has no historical data).
I was ranked #1 among 19 teams proposing a combination of machine learning models.
I was ranked #1 among 19 teams proposing a combination of machine learning models.
Rail-ticket Price Prediction
https://github.com/smarkochev/ds_notebooksTicket prices change based on demand and time, and there can be a significant difference in price. In these two notebooks, I investigated the possibility of developing a pricing monitoring system for Spanish high-speed trains using data from Kaggle datasets.
Notebooks:
• Rail_ticket_price_prediction_IDE.ipynb
• Rail_ticket_price_prediction_modelling.ipynb
Notebooks:
• Rail_ticket_price_prediction_IDE.ipynb
• Rail_ticket_price_prediction_modelling.ipynb
Statoil Kaggle Competition
https://github.com/smarkochev/ds_notebooksDrifting icebergs present threats to navigation and activities in areas such as offshore of the East Coast of Canada. In this competition, I was challenged to build an algorithm that automatically identifies if a remotely sensed target is a ship or iceberg.
Notebooks:
• Statoil_Kaggle_competition_main.ipynb
• Statoil_Kaggle_competition_google_colab_notebook.ipynb
• Statoil_Kaggle_competition_DL_comparison.ipynb
Notebooks:
• Statoil_Kaggle_competition_main.ipynb
• Statoil_Kaggle_competition_google_colab_notebook.ipynb
• Statoil_Kaggle_competition_DL_comparison.ipynb
Education
2008 - 2013
Ph.D. in Nuclear Physics
Moscow Institute of Physics and Technology - Moscow, Russia
2006 - 2008
Master's Degree in Applied Mathematics and Physics
Moscow Institute of Physics and Technology - Moscow, Russia
2002 - 2006
Bachelor's Degree in Applied Mathematics and Physics
Moscow Institute of Physics and Technology - Moscow, Russia
Certifications
OCTOBER 2019 - PRESENT
Probabilistic Graphical Models Specialization
Stanford University | via Coursera
JULY 2019 - PRESENT
Advanced Data Science with IBM Specialization
IBM | via Coursera
Skills
Libraries/APIs
Pandas, Scikit-learn, NumPy, SciPy, XGBoost, Matplotlib, TensorFlow, Keras, PyMC, Spark ML, OpenAI API, Beautiful Soup, Beautiful Soup 4, Google Maps API, REST APIs, PySpark, SpaCy, HubSpot API
Tools
ARIMA, AWS Step Functions, Terraform, ChatGPT, GitLab CI/CD, MATLAB, Looker, Git, LaTeX, Microsoft Power BI, Spark SQL, Amazon SageMaker, AI Prompts, GIS, LaunchDarkly, Jira, Microsoft Copilot
Languages
SQL, Python, Python 3, Snowflake, Octave, R, Looker Modeling Language (LookML), C++, SAS
Paradigms
Quantitative Research, Management, Business Intelligence (BI), Agile, Object-oriented Programming (OOP), ETL, Anomaly Detection, Automation
Platforms
Jupyter Notebook, Amazon Web Services (AWS), Visual Studio Code (VS Code), AWS IoT, Linux, Docker, AWS Lambda, HubSpot, Azure, Google Cloud Platform (GCP), Databricks, CrewAI
Storage
MySQL, PostgreSQL, Data Integration, Data Pipelines, Azure SQL, Microsoft SQL Server
Frameworks
AWS Serverless Application Model (SAM), LightGBM, Spark
Industry Expertise
Retail & Wholesale, Healthcare
Other
Applied Mathematics, Data Analysis, Digital Signal Processing, Machine Learning, Data Cleaning, Nonlinear Optimization, University Teaching, Software Development, Clustering, Applied Physics, Mathematics, Scientific Data Analysis, Data Analytics, Data Science, Data Visualization, Time Series, Artificial Intelligence (AI), Predictive Modeling, Statistics, Dash, Software, Classification Algorithms, Regression Modeling, Algorithms, Recommendation Systems, Revenue Management, Pricing, Big Data, Data Quality Analysis, Solution Architecture, Pricing Models, Dynamic Pricing, Forecasting, Large Language Models (LLMs), Mathematical Modeling, Data Preprocessing, Revenue Modeling, Modeling, Time Series Analysis, Random Number Generation, Data-informed Recommendations, Statistical Modeling, Analytics, Data Classification, Feature Engineering, Correlational Analysis, Predictive Analytics, Gradient Boosting, Time Series Data, Consulting, Classification, Data Warehousing, ETL Development, Model Evaluation, Data Cleansing, SQL, Monte Carlo Simulations, Deep Learning, Cython, Bayesian Inference & Modeling, Dashboard Development, Computer Vision, Multithreading, Unsupervised Learning, Natural Language Processing (NLP), A/B Testing, Generative Pre-trained Transformers (GPT), Data Scraping, CI/CD Pipelines, AI Agents, LangChain, Machine Learning Operations (MLOps), Transformer Models, Collaborative Filtering, AI Chatbots, Principal Component Analysis (PCA), Document Parsing, OpenAI GPT-3 API, OpenAI GPT-4 API, AI Model Training, OpenAI, Prompt Engineering, APIs, Data Architecture, Data Engineering, Reporting, Unstructured Data Analysis, Fine-tuning, Chatbots, Financial Modeling, Churn Analysis, Statistical Analysis, Data Build Tool (dbt), Back-end, Climate Change, Simulations, Monte Carlo, Localization, Looker Studio, Traffic Analysis, API Integration, Google BigQuery, Agentic AI, ETL Pipelines, ETL Tools, AI Automation, AI Consulting, Decision Modeling, Health, scoring, Multivariate Statistical Modeling, Cloud Platforms, Embedding Models, Data Modeling, Vector Databases, GeoPandas, Geographic Information Systems, Research, Property Valuation, Pricing, Pricing Models, Document Processing, Workflow Automation & System Integration, PDF Scraping, Amplitude, mParticle, FastAPI, Sensor Data, Conversational AI, eCommerce, Pricing Strategy, Competitor Analysis & Profiling, Competitive Strategy, Funnel Analysis, Simulators, Statistical Methods, Web Dashboards, Nuclear Physics, Spectroscopy, Mining, Monthly Recurring Revenue (MRR), ARPU, Microsoft Azure, Risk Models, Risk Modeling, Web Scraping, Website Data Scraping, Robotic Process Automation (RPA), RAG Systems, Inventory Management, ETL for AI
Collaboration That Works
How to Work with Toptal
Toptal matches you directly with global industry experts from our network in hours—not weeks or months.
1
Share your needs
Discuss your requirements and refine your scope in a call with a Toptal domain expert.
2
Choose your talent
Get a short list of expertly matched talent within 24 hours to review, interview, and choose from.
3
Start your risk-free talent trial
Work with your chosen talent on a trial basis for up to two weeks. Pay only if you decide to hire them.
Top talent is in high demand.
Start hiring