Data Science
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.
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.
What Our Data Science Services Include
- 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.
- 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.
- 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.
