Back to the main menu
Data Science Consulting Services
Data science services help companies run experiments on their data in search of business insights. An experienced data science partner, ScienceSoft leverages machine learning, artificial intelligence, and deep learning technologies to meet our clients’ most ambitious analytics needs.
Why ScienceSoft
- In data science, artificial intelligence, and machine learning since 1989.
- Practical experience in 30+ industries, including healthcare, BFSI, manufacturing, retail and ecommerce.
- A seasoned team of domain analysts, data scientists, and solution architects with 12–27 years of experience.
- In-house compliance experts to ensure adherence to HIPAA, GDPR, PCI DSS, and any other required global and local regulations.
-
Established project management practices to guarantee project success regardless of time and budget constraints.
- Partner to AWS, Microsoft, and Oracle.
- ISO 9001 and ISO 27001-certified to guarantee top software quality and complete protection of our customers’ data.
What Our Customers Say
Derek Carter
CEO
Overall, you have exceeded our expectations in every way.
Special credit to the data scientist: the performance of the new image stitching algorithm is amazing.
So far, we've been partnering with ScienceSoft for around 3 years, and we are satisfied with our cooperation and its results.
ScienceSoft's team undertook the development of our product from scratch. They delivered software in time and with the required quality.
We are satisfied with our cooperation with ScienceSoft and their skilled development team, which smoothly fit into our project.
The team identified core errors, which didn't allow efficient solution operation, and implemented high-speed convolutional neural networks to fix them.
Data Science Services We Offer
Data science consulting
Whether you need an ML model to solve a specific business task or plan to implement a complex data science solution, our industry analysts and data scientists are ready to provide you with an exhaustive consultation. With a detailed project roadmap and an optimal tech stack from us, you will get actionable steps to turn data into a value driver.
Data science solution implementation
Our experts build industry-centric data science solutions that foster informed decision-making, streamline operations, eliminate human work, increase safety, enhance customer experience, and ensure other data-driven benefits. For complex projects, we are ready to start with a PoC or an MVP.
Data science evolution
Our experts will provide you with strategic and tactical guidance if your data science solution needs to meet new challenging goals. We will design and implement new ML models, software features and modules to reinforce your solution with extra ML-powered capabilities.
Data science solution support
We regularly check your ML models for accuracy and adjust them to ensure high-quality insights and predictions. With proactive monitoring and efficient issue resolution by a trusted IT partner, you can be sure your solution functions seamlessly.
Complementary Data Science Services We Offer
Advising on and developing ML-powered solutions to help companies find hidden patterns in massive amount of data to enable accurate predictions and forecasting, root-cause analysis, automated visual inspection, etc.
Big data consulting, implementation, support, and big data as a service to help companies store and process big data in real-time as well as retrieve advance analytics insights out of huge datasets.
Retrieving valuable insights out of large, heterogeneous and constantly changing data sets without investing in in-house data mining talents.
Helping companies achieve informed decision-making and optimize processes through data-driven insights.
Consolidating disparate data into a single point of truth as the background for enterprise-wide analytics and automated reporting.
Our Data Science Portfolio
19 results for:
How Data Science Process Unfolds with ScienceSoft
1
Business needs analysis.
- Outlining business objectives to meet with data science.
- Defining issues with the existing data science solution (if any).
- Deciding on data science deliverables.
2
Data preparation.
- Determining data source for data science.
- Data collection, transformation and cleansing.
3
Machine learning (ML) model design and development.
- Choice of the optimal data science techniques and methods.
- Defining the criteria for the future ML model(s) evaluation.
- ML model development, training, testing and deployment.
4
ML model evaluation and tuning.
5
Delivering data science output in an agreed format.
- Data science insights ready for business use in the form of reports and dashboards.
- Custom ML-driven app for self-service use (optional).
- ML model integration into other applications (optional).
6
User & admin training, data science support consultations.
I think the success of data science projects relies heavily on the ability to translate customer goals into development requirements. Let's say you want to build a churn prediction model. It looks clear, but we need to delve deeper into your case to bring real value to your business. For example, if you aim to increase customer retention, we'll ensure the model predicts churn risk in real time so that you can intervene with corrective measures immediately. However, if you focus on enhancing customer lifetime value, our data science consultants may recommend incorporating lifetime value prediction alongside churn forecasting. This helps you see if preventing churn is worth the effort.
Use Cases ScienceSoft Covers with Data Science Services
Operational intelligence
Optimizing process performance due to detecting deviations and undesirable patterns and their root-cause analysis, performance prediction and forecasting.
Supply chain management
Optimizing supply chain management with reliable demand predictions, inventory optimization recommendations, supplier- and risk assessment.
Product quality
Proactively identifying the production process deviations affecting product quality and production process disruptions.
Predictive maintenance
Monitoring machinery, identifying and reporting on patterns leading to pre-failure and failure states.
Dynamic route optimization
ML-based recommendation of the optimal delivery route based on the analysis of vehicle maintenance data, real-time GPS data, route traffic data, road maintenance data, weather data, etc.
Customer experience personalization
Identifying customer behavior patterns and performing customer segmentation to build recommendation engines, design personalized services, etc.
Customer churn
Identifying potential churners by building predictions based on customers’ behavior.
Sales process optimization
Advanced lead and opportunity scoring, next-step sales recommendations, alerting on negative customer sentiments, etc.
Financial risk management
Forecasting project earnings, evaluating financial risks, assessing a prospect’s creditworthiness.
Patient treatment optimization
Identifying at-risk patients, enabling personalized medical treatment, predicting possible symptom development, etc.
Image analysis
Minimizing human error with automated visual inspection, facial or emotion recognition, grading, and counting.
What Goals Do You Want to Reach with Data Science?
Our competence and experience are not limited to the described use cases. Drop us a line, and our consultants will outline how data science can be applied in your case.
Benefits Our Customers Report
Lower equipment maintenance costs
due to AI-powered recommendations on parts replacement.
Minimized human factor errors
due to process automation powered by a custom AI algorithm.
Methods and Technologies We Use
To get to the valuable insights that your data hides, we apply both proven statistical methods and elaborate machine learning algorithms, including such intricate techniques as deep neural networks with 10+ hidden layers.
Methods
Statistics methods
- Descriptive statistics, e.g., to summarize customer data, identify outliers in stock prices, visualize equipment performance data.
- ARMA and ARIMA, e.g., to forecast sales, prices, demand, etc.
- Bayesian inference, e.g., to predict possible outcomes like equipment failure or disease likelihood and model spatial patterns.
Non-NN machine learning methods
- Supervised learning algorithms are good for classification and regression tasks, e.g., diagnosing based on image analysis or stock price prediction.
-
Unsupervised learning algorithms are good for clustering tasks, e.g., segmenting customers based on their purchase history or detecting fraudulent financial transactions.
-
Reinforcement learning methods are good for decision-making influenced by interaction with the environment, e.g., personalization engines responding to user behavior.
Neural networks, including deep learning
- Convolutional and recurrent neural networks (including LSTM and GRU), e.g., for NLP tasks.
- Autoencoders, e.g., to analyze medical images.
- Generative adversarial networks (GANs), e.g., to generate images that will be used for training ML algorithms.
- Deep Q-network (DQN), e.g., to optimize energy consumption, to recommend the best settings for manufacturing equipment.
- Bayesian deep learning, e.g., to improve speech recognition and translation accuracy.
Technologies
How Much Does a Data Science Solution Cost?
The cost of your data science initiative will depend on the service option you need and the overall project complexity.
Developing a separate data science component
Cost: $30,000–$200,000
End-to-end development of a data-science-based solution
Cost: $200,000–$600,000
The yearly cost of support services may be estimated as 15–25% of the initial development costs, while it may amount up to 70% of the TCO during the entire solution lifespan.
Estimate the Cost of Data Science Services
Please answer a few questions about your data science needs. This will help our experts calculate your quote quicker.
Our team is on it!
ScienceSoft's experts will study your case and get back to you with the details within 24 hours.
