Data Science as a Service
Data science as a service allows companies to get business insights leveraging advanced analytics technologies, including deep learning, without investing in in-house data science competencies.
Since 1989, ScienceSoft provides companies with data science services to enable them to exploit growth and process improvement opportunities.
Get Access to Advanced Analytics Techs and Skills
Optimize your business processes with effective root cause analysis and reliable forecasting delivered by ScienceSoft’s team on a regular or on-demand basis.
- 33 years in data science and data analytics.
- 9 years in big data.
- 17 years of experience in rendering data warehouse services.
- Hands-on experience with all major languages, libraries and cloud services for data science.
ScienceSoft USA Corporation is listed among The Americas’ Fastest-Growing Companies 2022 by Financial Times.
ISO 9001 and ISO 27001-certified to assure the quality of the data scienceg services and the security of the customers' data.
- Domain experience in 30 industries, including manufacturing, energy, retail and wholesale, professional services, healthcare, financial services, transportation and logistics, telecommunications.
- Root cause analysis and bottleneck recognition
- Forecasting of business performance metrics
Working capital management
Process, resource, cost management
Supply chain management
- Supply forecasting
- Demand forecasting
- Preventive alerting for inventory control
Demand and supply planning
- Demand and throughput forecasting
- Process quality prediction
- Production loss root cause analysis
Production process management
Overall equipment effectiveness
Overall resource effectiveness
- Root cause failure analysis and prediction
- Remaining useful lifetime prediction
- Predictive monitoring and preventive maintenance
Asset lifetime and uptime
Total productive maintenance
Maintenance and repair costs management
- Counterparty risk analytics
- Potential damage prediction
Credit risk management
Liquidity risk management
- Sentiment analysis
- Customer behavior prediction
- Sales forecasting
Cost per customer/lead
Revenue per customer
Lead conversion rate
Customer acquisition and retention
- Defect root cause analysis
- Production output predictive modeling with varying inputs
- Image and video analysis, automated visual inspection
Cost of quality management
Number of product recalls
Frequently Asked Questions
What value do we get when choosing data science as a service?
How can we be sure of the quality and speed of analytics insights?
DSaaS delivery is based on the agreed service quality KPIs, which may include:
Will our data be secure?
Data safety is ensured through:
Machine learning frameworks and libraries
Data science cloud services
Machine learning algorithms
Neural networks, including deep learning
Convolutional and recurrent neural networks (LSTM, GRU, etc.)
Autoencoders (VAE, DAE, SAE, etc.)
Generative adversarial networks (GANs)
Deep Q-Network (DQN)
Bayesian deep learning
Data Science Consulting for Electric Energy Consumption Analysis and Forecasting
ScienceSoft suggested high-level software architecture and provided detailed recommendations on creating machine learning models for electric energy consumption analysis and forecasting software, which would allow electric power companies to optimize their load management and price determination procedures.
Data Science Implementation for Sales Analysis and Forecasting
ScienceSoft supported a leading FMCG manufacturer by delivering science-based sales forecasting and attainable sales targets.
Big Data Implementation for Advertising Channel Analysis in 10+ Countries
ScienceSoft implemented a big data analytics system, which allowed one of the top market research companies to carry out comprehensive advertising channel analysis for different markets.
Development of a Big Data Solution for IoT Pet Trackers
To support a long-term customer in a new service launch, ScienceSoft delivered a scalable IoT data management solution that allowed processing 30,000+ events per second from 1 million devices.
Pricing Models for Data Science as a Service
Monthly subscription fee
Recommended when the engagement scope is clear, for outsourcing a particular number of data science talents to perform the required activities.
Time and Material
Recommended when the engagement scope is unclear.
For companies with no data science capabilities
- Analysis of business needs driving the company to apply data science.
- Source data preparation and cleansing.
- Development, training, testing and deployment of machine learning models.
- ML model tuning.
- Delivering data science output in an agreed format.
- Integrating ML models into an application for users’ self-service, if required.
For companies that need to enhance their data science insights/initiatives
- Analyzing your business needs and the existing data science environment.
- Evaluating the existing ML models, ML debugging and error analysis.
- Data cleansing.
- Continuous tuning and training of ML models for increased ML model accuracy.
- Adding new data to the ML models for deeper insight.
- Building new ML models to address new business and data analytics questions.
Why Turn to DSaaS Right Now
An expert data science team can help you quickly embrace data science for meeting particular advanced analytics objectives and achieving the following benefits:
Up to 30%
Reduction of equipment maintenance cost due to predictive monitoring and preventive maintenance.
Up to 20%
Increased product throughput and improved on-time delivery due to demand and throughput forecasting and production process optimization.
Up to 35%
Increase of product quality in discrete manufacturing with defect root cause analysis and product quality predictive modeling.
Reduction of inventory management costs due to the AI-based forecasting of demand-driving factors.
Consumers willing to shop more frequently due to customer behavior prediction and forecasting.
Support Decision-Making with Advanced Analytics
Cooperate with ScienceSoft to incorporate machine learning (including deep learning) capabilities into your business workflows without investing in building in-house data science teams and competencies.