Tulip Technologies | Data Science

Data Science

Data science services include data science consulting, development and support to enable companies to run experiments on their data in search of business insights. Since a long, TULIP has been applying data science in its different forms ranging from statistics to machine learning (including its most recent technique – deep learning) to meet the most deliberate analytics needs of our clients.

USE CASES WE COVER

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.

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.

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.

Data preparation.
- Determining data source for data science.
- Data collection, transformation and cleansing.

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.

ML model evaluation and tuning.

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).

User & admin training, data science support consultations.

COOPERATION MODELS WE OFFER

Data Science Solution Implementation

Do you consider building a data science solution in your company, but lack needed experience and resources? ScienceSoft is ready to implement its best practices to ensure a smoothly functioning data science solution that suits your business needs.

Data Science Improvement Consulting

If you’ve encountered a problem (e.g., noisy or dirty data, inaccurate predictions, etc.) in your data science project, we can serve as your think tank to help you figure out how to make your data science solution work as expected and bring the desired ROI.

Data Science Ongoing Consulting and Support

If you seek continuous support and evolution of your data science initiative, our team will closely cooperate with your subject matter experts and implementation team to provide ongoing recommendations and ensure the models’ continuous improvement. This will increase the quality of insights and help adjust the models to the changing environment.

Data Science As A Service (DSaaS)

Getting advanced data analytics insights derived with data science technologies or enhancing the existing data science initiatives without investing in in-house data science competencies.

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
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