We offer top-flight Data Science development services enabling companies to analyze their data, form valuable business insights, and build accurate predictions. Mediterra team comprises qualified data engineers and software developers having 6 to 10 years of commercial experience in Data Science projects as well as solid academic background. We deliver customized Data Science solutions, including data structuring, statistical modeling, predictive analytics, while leveraging the power of machine learning and deep learning technologies.
Our qualification is backed up with:
Data Science makes it possible to extract meaningful insights from vast sets of unstructured, messy data that cannot be processed by conventional methods. With the help of innovative approaches such as machine learning, deep learning, and predictive analytics, data scientists develop algorithms able to structure and analyze these data sets, and a result, identify relationships, and patterns that determine business performance.
By hiring our dedicated team of Data Science experts, your company will get solutions tailored to the specific needs of your industry that can help reveal new opportunities and empower making better decisions driven by data.
Building, training, and deploying Machine Learning models into real-life business environments.
Development of algorithms based on artificial neural networks for more complex data processing purposes.
Applying statistical methods to make reliable future predictions and discover actionable insights.
Ensuring databases operate smoothly by performing administration, maintenance, and optimization activities.
Creating applications capable of understanding and processing human language to gain valuable information.
Developing models for visual recognition to extract data from videos and images in a meaningful way.
We have a long record of delivering successful projects in Finances, Sports, Telecommunications, Agriculture, Engineering, Retail, and other industries.View All
The goal of the project was to define characteristics of the general traffic of store visitors, including traffic volume, time of entrance and exit, how long visitors stay on a trading floor, as well as personal visitor characteristics to identify regular customers and clients’ traffic maps. From the technical viewpoint, we used data from 2 video cameras, Ubuntu as the main OS and Python with such frameworks as Caffe, Pytorch, Numpy, Sklearn, Scipy, and OpenCV. As a result of the project, the client managed to boost store revenues by 16%.Learn more
The goal of the project was to build a platform to analyze video of a tennis game in order to break up games into shorter videos and remove those parts of the match where the players did not play (the players’ rest, the gap between the points, etc.). This allowed game statisticians to make further revisions of the game much faster. The logic for breaking up videos was based on the analysis of game events, position, speed and posture of players, ball movement and location, and other parameters. We used the following Data Science and Computer Vision techniques: optic flow, background subtraction, HoG detector, pose detection, and others. The project resulted in building a successful MVP, generating funds at around $300,000 for the further enhancement of this platform, and competing with such players as PlaySight.Learn more
The business goal of the project was to build a system that parses resumes and jobs and finds matches between specific job descriptions and applicants’ resumes based on age, gender, educational and professional background. The parsing part involves a complex solution based on the Apache Tika toolkit, ontologies (for skills, cities, universities, and so on), and NLP techniques. For matching, the system uses machine learning algorithms utilizing different information sets extracted from resumes and job systems. The trained model system ranks custom resumes/jobs and finds top N jobs/resumes that bring together the most matches. The system has an interface for retraining matching models based on new or updated resumes and jobs. Also, the system provides a RESTful API interface that allows external systems to use its functionality.Learn more
The goal of the project was to develop a Machine Learning module within the CF Engine platform intended to model complex financial products (RMBS, ABS, CLO, etc.). The developed module reviews legal documents against created models and automatically highlights the most important information. To perform the task, the system based on Machine learning and Natural Language Processing algorithms parses a specific document, checks whether the document contains all required information, and if the document fits one of the created models, the system extracts the most important information and provides it as a summary for the user to review.Learn more
We offer our clients flexible cooperation options based on their preferences and project specifics that allow them to save costs. We respect our clients’ confidentiality and are ready to work under NDA.
A budget-friendly and low-risk model used when the scope and requirements of the project are well defined and documented.
Better suited for projects with less clear specifications and timelines, and provides the possibility to balance team size, as well as project workloads.
This model is used for long-term contracts, where a dedicated team of specialists is allocated for the project with an agreed fixed monthly payment for the engaged human resources.