Data Science and ML

Optimize enterprise operations with mathematical precision


Our clients

Finance & Insurance
Introduction to Algorithmic Marketing
Introduction to Algorithmic Marketing
Introduction to Algorithmic Marketing is a comprehensive guide to advanced marketing automation for marketing strategists, data scientists, product managers, and software engineers.

A Plug-and-play Platform for In-App and In-game Experience Personalization

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Why Grid Dynamics

Industry specializations

The commercial success of data science and enterprise AI projects often depends on domain expertise, at least as much as on statistical and machine learning skills. Grid Dynamics has experts, blueprints, and starter kits that are laser-focused on specific business functions, such as marketing and supply chain, and industries, such as retail, manufacturing, and finance.

ROI models and reference implementations

At Grid Dynamics, we are highly focused on delivering business value to our clients in a predictable way. We have developed unique ROI models to streamline typical data science engagements and created reference implementations that enable us to evaluate multiple standard models and techniques promptly.

Global data science talent

We have spent more than a decade building a global network of data engineering and data science professionals. This enables us to staff data science projects rapidly, involve top talent, and provide our clients with flexible options regarding the location and composition of engineering and consulting teams.

Rapid prototyping

Solid reference implementations, global engineering networks, and experience with multiple industry and enterprise domains, enables us to develop and deliver prototypes and minimum viable products (MVPs) in a matter of weeks. We readily offer proof-of-concept events for the toughest problems.

Experience with industry leaders

We have worked with S&P 500 companies on the most challenging engineering and data-related problems for more than a decade. We offer unique insight into the state of the industry, world-class best practices, and development methods used by the leading and most innovative companies across the globe.

Cutting-edge technology

We invest heavily in researching enterprise AI problems, studying how the latest deep learning, reinforcement learning, computer vision, and other technologies can be applied to marketing, supply chain, manufacturing, and IT use cases. This effort, combined with our unique industrial experience, helps us to deliver truly innovative and highly efficient solutions.

Case studies & Whitepapers

Personalizing in-game experience using reinforcement learning

- Personalize in-game experience
- Reduce model development effort
- Increase long-term engagement / LTV
Grid Dynamics' solution
- Reinforcement learning based personalization platform
- MVP delivered in 8 weeks

Up to 25% dollar-per-user improvement compared with the baselines

Price optimization for video games using machine learning

- Optimize promotions across many channels and countries 
- Forecast the demand 24-month ahead
- Properly handle new game releases
Grid Dynamics' solution
- Demand forecasting models
- What-if analysis tools for promotion scenarios
- MVP delivered in 6 weeks

- Manual process replaced by data-driven optimization
- Increased promotion efficiency compared with the baselines

The Essential Guide to Transforming IT Operations with AIOps

Modern IT operations have to deal with dynamic mixes of public cloud platforms and services, cloud-native and serverless applications, and on-premise deployments. These systems, services, and applications generate enormous amounts of data that are challenging to collect, analyze, and use for issue detection and remediation. In this white paper, we discuss how this challenge can be addressed using machine learning and artificial intelligence methods, what aspects of IT operations can be improved using such techniques, and how companies should plan their capability roadmaps in this area. 

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Building a next best action model using reinforcement learning
In this article, we discuss how traditional targeting and personalization models such as look-alike and collaborative filtering can be combined with reinforcement learning to optimize multi-step marketing action policies (aka Next Best Action policies). These techniques can be applied to various business problems such as customer retention, customer experience optimization, and promotion targeting.
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Predictive analytics for promotion and price optimization
This article describes a reference implementation of a price and revenue management tool that showcases the main capabilities of AI-based price optimization software and features several advanced techniques. We start with a strategic vision of a price analytics solution, then build a demand model, and finally develop a fully automated optimization application.
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Deep reinforcement learning for supply chain and price optimization
This article is a hands-on tutorial that describes how to develop, debug, and evaluate RL optimizers using PyTorch and RLlib. This approach can be applied to a number of supply chain problems such as stock level optimization, transportation cost minimization, and warehouse space optimization.
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