Data Scientists for Forecasting, Computer Vision and LLM Evaluation

Cortance matches companies with vetted data scientists who have already worked on the kind of data your project runs on. That can be drone and satellite imagery, sensor streams for navigation, imbalanced fraud datasets, or LLM output that has to be evaluated before it ships. Every data scientist passes the same five vetting stages, and the match starts from your data and your industry, not from a job title.

What's in the toolkit?

The specialists here cover predictive modelling and forecasting, classification, clustering, anomaly detection, recommendation engines, and NLP. Core stack: Python, scikit-learn, XGBoost, LightGBM, PyTorch, and TensorFlow, plus Pandas and SQL for the unglamorous data-wrangling half of the job. Across the five data scientists in our network, average experience is about six years, as of October 2026. They don't stop at a notebook, either. Dockerfiles, CI/CD, model versioning with MLflow, and drift monitoring after launch are part of the job description here, the parts that turn a demo into something running against production traffic.

Match by industry before the first call

Fintech shows up often: credit risk modelling, trading automation, financial data analysis. Healthcare too: medical image classification, patient data analysis, biomedical document processing. Agritech is a speciality for a few, built around drone and satellite imagery for yield forecasting and crop health monitoring. Add defence, manufacturing, gaming analytics, and sports tech to the list, plus general SaaS products for teams that need forecasting or dashboarding rather than a niche vertical. Industry history is visible on every profile, so you match before you reach out instead of finding out three weeks in that nobody on the shortlist has touched a regulated dataset.

Recent work from this network

1. Precision agriculture. Michael Y. built a geo-temporal drone image alignment method for SeeTree, later patented, that automated 85 percent of the company's data processing pipeline, then followed it with a separate anomaly detection system for crop health monitoring from drone imagery.
2. Defense navigation. Alexander C. developed visual inertial navigation for GPS-denied drones, reaching about 4 meters of error on 1 km simulated missions and 2 to 5 percent positional error in flight on Raspberry Pi hardware.
3. Fraud detection. Viktoriia A. built a semi-supervised learning system for a blockchain platform that flags malicious transactions inside a highly imbalanced dataset, deployed with MLflow and MongoDB.

Generative AI work is already part of the Data Scientist's job

Our expert Viktoriia A. uses GenAI tools heavily in her work. Beyond the fraud detection system above, she has run a research project establishing best practices for production-grade LLM applications: prompt engineering experiments, a Retrieval-Augmented Generation pipeline, and an evaluation framework for checking output quality before it ships. If your project builds on foundation models rather than training one from scratch, we have a profile that matches. Data scientists increasingly pair traditional modelling with AI-assisted tooling to move faster without skipping validation, and that is the kind of data scientist we prefer to match.

Share your requirements through the platform or directly with a hiring manager, and Cortance's matching system scores candidates against your stack, domain, seniority, and availability, then delivers a shortlist within 30 minutes so you can complete a hire within two days.

Our data scientists work remotely on a flexible or long-term basis. As of October 2026 they are based in Ukraine, Portugal, Spain and Georgia, which overlaps well with EU and US East Coast hours.