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Data Science Solutions

Our data science process is carried out by experts with extensive experience in data engineering and solving complex business challenges. We help you make data-driven decisions, improve the user experience, and more. Get the most out of data mining, machine learning, and other DATAFOREST data science services.

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Data science professional services include

We offer data science services customized to businesses, using advanced analytics and machine learning to extract valuable insights from their data. DATAFOREST helps make data-driven decisions, optimize processes, and gain a competitive edge in the respective industries.

Let your data make value

Data science professional services include 

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Predictive analysis

We bring you value by extracting insights from historical data. Our forecasting models can help you with improving Sales, Inventory and Process management. What is more, empower your Marketing and User Experience by applying product recommendation engines and buyer behavior prediction.
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Optimization models

We help various industries with resource optimization. It helps increase the management efficiency of leftover stock goods, improve supply chain and logistics, and allocates time and other resources. 
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Machine Learning

Our machine learning algorithms analyze vast amounts of data and learn patterns within that data. This allows businesses to predict future outcomes based on historical data, which can be invaluable to traditional mathematics methods.View page
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Risk management

Our data science company provides scoring, fraud and anomaly detection solutions to predict and protect your business from third party attacks and unexpected data loss.
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Data science as a service

DATAFOREST data science as services allows you to get the most out of analytics, deep learning, insights finding, and more without building your data science competencies within the company. 
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Natural language processing

Our developers provide NLP tools for your business to help you gain more from the data with Text extraction, Sentiment Analysis, Text Classification. In other words, we can parse PDFs, extract key topics, understand clients’ intent and make a structure from your unstructured text.  
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BI & data visualization

Visual charts, tables and dashboards allow you to understand the information collected from various data resources at a glance. Catch your insights with DATAFOREST.  
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Customer profiling

We determine your ideal client profile based on the data you possess. We increase the performance of your marketing campaigns and sales by targeting the most motivated leads. 
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Data science consultancy

Use our data science consulting services to learn how you can benefit from the data. What would be most relevant to your business now, and how to smoothly implement data science solutions into your existing system. 
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AI Integration

We offer expertise in developing and implementing AI analytics, machine learning models, and data management strategies to enhance businesses' operational efficiency and decision-making through data-driven insights and automation.
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Success stories

Check out a few case studies that show why DATAFOREST will meet your business needs.

Emotion Tracker

The financial company wanted to create a web app for their customers to query analytics about various banks with custom dashboards and analytics features. To solve this challenge, we developed a web-native application from scratch using highly-loaded AI scraping algorithms to generate a real-time database of banking data from various open-source websites and financial institutions. The app is easy to use and provides valuable insights.
15%

CX improvement

7%

cost reduction

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Alex Rasowsky

CTO Banking company
View case study
Emotion Tracker preview
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They delivered a successful AI model that integrated well into the overall solution and exceeded expectations for accuracy.

Client Identification

The client wanted to provide the highest quality service to its customers. To achieve this, they needed to find the best way to collect information about customer preferences and build an optimal tracking system for customer behavior. To solve this challenge, we built a recommendation and customer behavior tracking system using advanced analytics, Face Recognition, Computer Vision, and AI technologies. This system helped the club staff to build customer loyalty and create a top-notch experience for their customers.
5%

customer retention boost

25%

profit growth

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Christopher Loss

CEO Dayrize Co, Restaurant chain
View case study
Client Identification preview
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The team has met all requirements. DATAFOREST produces high-quality deliverables on time and at excellent value.

Entity Recognition

The online marketplace for cars wanted to improve search for users by adding full-text and voice search, as well as advanced search with specific options. We built a system application using Machine Learning and NLP methods to process text queries, and the Google Cloud Speech API to process audio queries. This helped greatly improve the user experience by providing a more intuitive and efficient search option for them.
2x

faster service

15%

CX boost

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Brian Bowman

President Carsoup, automotive online marketplace
View case study
Entity Recognition preview
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Technically proficient and solution-oriented.

Employee Tracker

The large Retail company was facing a significant challenge in managing and tracking our employees' working hours and needed a solution that would automate the process and ensure accuracy. We developed a system for counting employees' working hours. Employees simply approach the device upon arrival and the system automatically identifies them and records their check-in time.
100h+

manual work reduced

13%

work experience boost

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Bernd Herzmann

CTO Retail company
View case study
Employee Tracker preview
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DATAFOREST has an excellent workflow and provide constant and close communication. The team brings in a range of technical talent to address issues as they arise.

AWS Cost Reduction

This project optimized the cloud infrastructure of a U.S. IT services company to reduce costs and improve performance. Our investigation identified several areas for optimization, including unused computing resources, inconsistent storage, and a lack of savings plans. We helped to optimize resources, implemented better policies for storage, and improved internal traffic flow through architecture redesigns and dockerization.
23k+

monthly savings

8%

performance optimization

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Harris N.

CTO IT Services & Consulting
View case study
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The team's deep understanding of our needs allowed us to achieve a more secure, robust, and faster infrastructure that can handle growth without incurring exorbitant costs.

Insurance Profitability Analysis Tool

This project involved developing a tailor-made data analysis tool for a U.S. insurance provider who were facing challenges analyzing a significant volume of data. The Client needed a professional and customized solution which would enable effective analysis of their data and provide actionable insights to improve their business operations. Our solution delivers real-time processing of data, flexible filtering capabilities through dashboards, and also supports dashboards detailing the evaluation of insurance loss or profit by industry vertical. Additionally, a predictive model for profitable insurance cases was built using historical data, and a reporting system was created to show significant factors and profitability based on different metrics.
> 10TB

data processed

89%

accuracy improvement

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Sean B.

CEO Insurance provider
View case study
Insurance Profitability Analysis Tool case preview image
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Great work! The team provided an excellent solution for consolidating our data from multiple sources and creating valuable insights for our business.

Stock relocation solution

The client was faced with the challenge of creating an optimal assortment list for more than 2,000 drugstores located in 30 different regions. They turned to us for a solution. We used a mathematical model and AI algorithms that considered location, housing density and proximity to key locations to determine an optimal assortment list for each store. By integrating with POS terminals, we were able to improve sales and help the client to streamline its product offerings.
10%

productivity boost

7%

increase in sales

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Mark S.

Partner Pharmacy network
View case study
Stock relocation preview
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The team reliably achieves what they promise and does so at a competitive price. Another impressive trait is their ability to prioritize features more critical to the core solution.

Financial Intermediation Platform

The project aims to develop a deal origination platform for private equity investments in infrastructure-related sectors and involves building a secure, interactive B2B platform from scratch, with sign-up functionality to connect investment firms to proprietary investment opportunities. DATAFOREST built a highly-loaded platform and applied AI functionality to empower the application's development.
98%

model accuracy

100%

timely development

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Enrico Cattabiani

Founder & CEO IDN, Infrastructure Deals Network
View case study
Financial Intermediation Platform preview
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They understood our requirements, translated into actions rapidly, and adapted to requests easily.

Store heatmap

The electronics retailer, wanted to improve their sales and customer service by analyzing the flow of people into their stores. We created a system using Machine Learning, image detection, and face recognition. The system tracks visitors' movements and the most viewed shelves and products. This information helps the store to focus on selling popular products and to avoid unpopular ones, ultimately improving the sales process.
9%

increase in sales

100%

dead zones removed

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Jared D.

CEO Consumer Electronics Retail
View case study
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DATAFOREST provides meaningful shopper-behavior Insights. They are very responsive and effective, trying to engineer and offer the best fit solution.

Demand forecasting

We built a sales forecasting system and optimized the volume of goods in the warehouse and the range of goods in different locations, considering each outlet's specifics. We set up a system that has processed more than 8 TB of sales data. These have helped the retail business increase revenue, improve logistics planning, and achieve other business goals.
88%

forecasting accuracy

0.9%

out-of-stock reduced

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Andrew M.

CEO Luxury Goods Retail
View case study
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I think what is really special about the DATAFOREST service is its flexibility, openness, and level of quality and expertise.

Show all Success stories

5 Steps From Data Science Service to Business Insights

How do we help companies?
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Step 1 of 5

Free consultation

It's a good time to get info about each other, share values and discuss your project in detail. We will advise you on a solution and try to help to understand if we are a perfect match for you.
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Step 2 of 5

Discovering and feasibility analysis

One of our core values is flexibility, hence we work with either one page high level requirements or with a full pack of tech docs.  

In Data Science, there are numerous models and approaches, so at this stage we perform a set of interviews in order to define project objectives. We elaborate and discuss a set of hypotheses and assumptions. We create solution architecture, a project plan, and a list of insights or features that we have to achieve.
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Step 3 of 5

Solution development

The work starts with data gathering, data cleaning and analysis. Feature engineering helps to determine your target variable and build several models for the initial review. Further modeling requires validating results and selecting models for the further development. Ultimately, we interpret the results. Nevertheless, data modeling is about a process that requires lots of back and forth iterations. We are result focused, as it’s one of our core values as well. 
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Step 4 of 5

Solution delivery

Data Science solutions can be a list of insights or a variety of different models that consume data and return results. Though we have over 15 years of expertise in data engineering, we expect client’s participation in the project.  While modeling, we provide midterm results so you can always see where we are and provide us with feedback. By the way, a high-level of communication is also our core value
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Step 5 of 5

Support and continuous improvement

We understand how crucial the solutions that we code for our clients are! Our goal is to build long-term relations, so we provide guarantees and support agreements. What is more, we are always happy to assist with further developments and statistics show that for us, 97% of our clients return to us with new projects.
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E-commerce

DATAFOREST improves client experience through customer sentiment analysis, integration for omnichannel communication, and personalization based on behavior. The introduction of back-office automation gives data-driven marketing, system integration, and custom dashboards. Recommendation systems, demand forecasting, and dynamic price management stimulate sales.

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Retail

Big Data and data science enables the analysis of vast supply chain data, including historical sales, production, and transportation data, applied to market trends and economic indicators to forecast future demand. Using statistical models and optimization algorithms, data scientists determine optimal inventory levels, reorder points, safety stock levels, and replenishment strategies. Insights guide retail businesses in making data-driven decisions about product development, marketing strategies, customer experiences, and personalization initiatives.

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Finance

The main challenges in this industry are being overcome with the help of data engineering, business automation, and DevOps software production philosophy. With the help of artificial intelligence (AI) and data science technology, companies develop custom software to optimize internal operations, eliminate manual and time-consuming tasks, and improve overall productivity. Routine automation requires rule-based tasks and processes using robotic process automation, the combining of which reduces labor costs uses custom software development with specialized functionality, and decreases human error, which can result in costly mistakes.

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Insurance

The collaboration of data scientists and DevOps facilitates the continuous delivery of data-driven solutions and utilizes predictive models, machine learning, and data science tools to extract insights from insurance data. Effective data integration empowers new data product development, including predictive analytics tools, fraud detection systems, and customer self-service portals.

Cyber Security

Cyber Security

Data management is critical for identifying and responding to threats effectively in the cyber security industry. Data scientists work closely with DevOps teams to create data pipelines that collect and process security-related data from various sources to access and analyze relevant data efficiently, leading to the development of advanced warning detection models, anomaly detection systems, and security analytics tools.

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Pharma

Data science analyzes historical trial data, identifies relevant patient populations, integrates patient records to develop predictive models, and mines vast amounts of biomedical literature, clinical data, and molecular databases. Machine learning is also widely used in this industry. Data management practices permit data scientists to leverage reliable and relevant data for their analyses, supporting drug discovery, clinical trials, personalized medicine, adverse event analysis, and pharmacovigilance.

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Advertising and Marketing

Influencer data scraping calls for extracting and analyzing data from social media platforms to identify influencers with significant reach, engagement, and impact on their followers. Data scientists build predictive models to forecast customer churn, enabling marketers to take proactive retention measures. These models identify the key drivers that impact customer retention: product preferences, pricing sensitivity, or customer service satisfaction. Data science techniques such as clustering, segmentation, and predictive modeling analyze and classify customers.

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Real Estate

Data science techniques help real estate accurately value properties and determine optimal pricing. Data scientists provide insights into emerging real estate markets, investment opportunities, and potential risks. Machine learning algorithms suggest personalized property recommendations to potential buyers or renters.

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Data Science will increase work efficiency. Get expert support!

What methods does DATAFOREST use?

The methods of mathematical and statistical analysis, as well as knowledge of probability theory, can be conditionally divided as follows:

Statistics methods

Non-NN machine learning

Neural networks, including deep learning

What Data Science technologies do we use?

The tool for implementing the methods in the data science service is the program code, which can be divided into levels of integration as follows:
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Pandas
SciPy icon
SciPy
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TensorFlow
Numpy icon
Numpy
ADTK icon
ADTK
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DBscan
G. AutoML icon
G. AutoML
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Keras
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MLFlow
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Natural L. AI
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NLTK
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OpenCV
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Pillow
PyOD
PyOD
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PyTorch
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FB Prophet
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SageMaker
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Scikit-learn
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SpaCy
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XGBoost
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YOLO
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We will teach Data Science algorithms to solve your problems

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Reasons why DATAFOREST is worth being your data science company

DATAFOREST solves applied tasks through the data science service. We use new and time-tested methods of working with Big Data at the intersection of mathematics, computer science, and business needs. We have three reasons to offer ourselves as your assistant.
01

Experienced and qualified team:

we can save tens of thousands of dollars per month, process tens of terabytes of data, increase customer experience by 15%, and much more
02

Solution flexibility based on a high level of communication:

mutual trust leads to decision optimization — reduction of manual labor by 100+ hours, increased production and sales productivity, and close to 100% forecasting accuracy
03

Positive feedback from customers with whom we have already had the honor to cooperate:

despite the complexity of the tasks, customers are satisfied with our solutions; evidence of this is the positive reviews on Clutch, GoodFirms, and Upwork.

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Still have questions about data science services?

What is data science, and why is it needed?
It is the science of big data analysis methods. The data science service aims to extract useful information for the enterprise business and the service sector. Since there are many data on the Internet, and it becomes more and more every day, this is called Big Data. The science doesn't imply the expected result after setting the algorithm to the machine, but the installation of fundamental principles, and the machine learns itself. It is machine learning, and DATAFOREST uses it. To improve the performance of machine learning models by providing additional training examples, generative AI can be used.
How does the data science services company solve problems?
The applied data science process is based on the methods of mathematical and statistical analysis, as well as knowledge of probability theory. Applied data science consulting services have two tasks: understanding what is happening with the data now and what it promises in the future. Theoretical approaches are implemented by DATAFOREST in practice using programming tools (languages, frameworks, and libraries) and services for extracting, processing, storing, and visualizing data.
How does a data science company service grow a business?
Whoever owns the information owns the world. Proper analysis and visualization of Big Data give an increase in margins and an advantage over competitors who have yet to turn to experts with data science specialization. Transaction analysis, loyalty program, customer behavior forecasting — this is what DATAFOREST engineers work with, taking into account the benefits of data science for business.
How much does a data science service cost?
No one will give an exact answer because the cost depends on the project's complexity. But by general principles, structures with a limited budget - startups and government agencies - often use data science engineering. In addition, data science tools involve saving resources, and the number of data scientists is constantly increasing along with the competition. So, the DATAFOREST answer to the question is no more than money.
Data science vs. data analytics: what is the key difference?
While data science and analytics share some similarities, such as data analysis techniques, the main distinction lies in data science's broader scope and objectives. Data analytics is focused on understanding historical data, while the application of data science encompasses a broader range of activities, including predictive modeling, machine learning, and automation.
Data science and data engineering: how are they different?
Data engineering and data science are complementary roles that work closely together to enable data-driven decision-making. Data engineers focus on efficiently managing data infrastructure and ensuring data availability and reliability, while data scientists focus on extracting insights and building models to derive value from the data.
What qualifications and expertise do your data science consultants possess?
They have proficiency in programming languages commonly used in components of data science, such as Python and R, and familiarity with other tools, such as SQL, Java, Scala, or MATLAB. The team also firmly understands statistical concepts, hypothesis testing, probability theory, and experimental design. They are good at exploratory data analysis techniques to gain insights from data. The teammates are knowledgeable about supervised and unsupervised machine learning methods and techniques such as regression, classification, clustering, dimensionality reduction, and recommendation systems.
How long does it typically take to see tangible results from your data science services?
Data science is an iterative and evolving field; tangible results can often be observed throughout the project's lifecycle. Initial insight from data science, preliminary model, and incremental improvement provide value along the way, even before the final solution is fully developed. The timeline for tangible results in data science professional services highly depends on each project's unique circumstances and complexities.

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