Data Science Consulting Services
Data science services help companies run experiments on their data in search of business insights. ScienceSoft renders data science consulting leveraging Machine Learning, Artificial Intelligence, and Deep Learning technologies to meet our clients’ most deliberate analytics needs.
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.
Dymanic 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.
Two years ago, we commissioned ScienceSoft to audit and upgrade our partially developed AI-based software for clay pigeon shooting tracking.
ScienceSoft ramped up a development team consisting of two C++ developers, two data scientists, and a UI design expert to fulfill the project. The team identified core errors, which didn’t allow efficient solution operation, and implemented high-speed convolutional neural networks to fix them. As a result, the system could track a flying target in a real-life outdoor environment and faultlessly detect shooter’s performance.
Simen Løkka, CEO, Travision AS
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.
Data science solution implementation
- Easy access to the required experience or resources.
- Building a smoothly functioning data science solution tailored to your unique business needs.
Data science improvement consulting
- Strategic and tactical guidance.
- Overcoming problems (noisy or dirty data, inaccurate predictions, etc.) in a data science project.
Ongoing data science consulting and support
- Continuous support and evolution of your data science initiative to increase the quality of insights.
- Adjusting the ML models to the changing environment.
- No investment in in-house data science competencies.
- Getting advanced data analytics insights derived with data science technologies or enhancing the existing data science initiatives.
Methods
Statistics methods
- Descriptive statistics
- ARMA
- ARIMA
- Bayesian inference, etc.
Non-NN machine learning methods
- Supervised learning algorithms, such as decision trees, linear regression, logistic regression, support vector machines.
- Unsupervised learning algorithms, for example, K-means clustering and hierarchical clustering.
- Reinforcement learning methods, such as Q-learning, SARSA, temporal differences method.
Neural networks, including deep learning
- Convolutional and recurrent neural networks (including LSTM and GRU)
- Autoencoders
- Generative adversarial networks (GANs)
- Deep Q-network (DQN)
- Bayesian deep learning
Technologies
Programming languages
10 years
Projects
50+
30
ScienceSoft's Python developers and data scientists excel at building general-purpose Python apps, big data and IoT platforms, AI and ML-based apps, and BI solutions.
34 years
40
ScienceSoft's C++ developers created the desktop version of Viber and an award-winning imaging application for a global leader in image processing.
Frameworks
Libraries
Related Data Science Services We Offer
Advising on and developing ML-power 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 an in-house data mining talents.
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