Data Science Consulting Services
For 30 years, we have been applying data science in its different forms ranging from statistics to machine learning (including its most recent technique – deep learning) to run experiments on your data in search of precious insights.
Benefits that Data Science Brings
We, at ScienceSoft, are devoted to data science services as we see many improvements that it can bring to businesses, regardless of the industry they represent.
Our data scientists can apply an ARIMA model or a deep neural network to generate reliable demand predictions. We can build neural networks or apply ML algorithms, such as hierarchical clustering and multi-class support vector machines, to evaluate your suppliers and assess the risks associated with each of them.
We can help you fight low overall equipment effectiveness (OEE) by identifying the root causes for availability, performance and quality losses. We apply machine learning techniques to achieve predictive maintenance, undisrupted functioning, and enhanced product quality.
- Predictive maintenance
Our data scientists can analyze the data from sensors installed at monitored machinery parts to understand the patterns in machinery functioning so that you could plan its maintenance more efficiently. One of the ways to solve this task is to apply Naïve Bayes algorithm to classify normal and pre-failure events. To get more insights, we can further classify the cases based on the time left until a breakdown. For instance, a failure that can happen within 24 hours gets a red color, a failure within 24h – 72h – yellow, a failure in more than 72h – green color.
- Operational intelligence
Even if the monitored parameters seem to be fine when considered individually, their particular combination can still witness an upcoming breakdown. With the help of data science, we can identify such hidden interdependencies and their potential consequences. As a result, operators can receive real-time alerts and solve issues themselves or escalate them to the attention of the maintenance team.
- Enhanced product quality
Machine learning techniques are helpful when you need to identify process disruptions at each production stage. We compare the actual and expected duration of each operation, as well as check temperature, vibration and other parameters. All this, to move beyond simple thresholds to really complex dependencies and identify the deviations that may affect product quality as early as possible. Having learned at some early stages that raw materials or parts are defective, you won’t waste your time and resources on continuing with them at all the remaining production stages. This can also save the entire manufacturing process from being affected by a defective part or component.
Applying machine learning techniques, such as collaborative or content-based filtering (or both of them combined), we can design a recommendation engine to boost the sales of your ecommerce store. Such an engine can help you make your customers happier with relevant product offers. A webpage showing personalized content, a mobile app with promo offers that spark customers’ interest, as well as relevant email campaigns are also among the gains that you can get with data science.
We can implement machine learning-based lead and opportunity scoring so that you’re sure that your sales team adheres to the chosen business strategy, as well as wisely prioritizes their efforts.
Besides, we can create a machine learning model to make your communication with customers stellar. Trained to detect attitude markers and recognize your customers’ mood, the model will signal your sales team if a particular customer experiences negative emotions.
We can provide you with a machine learning model to help you streamline the sales process by providing your sales team with clever next-step recommendations.
We’ll apply machine learning algorithms to provide you with accurate predictions of your customers’ behavior. For example, you’ll be able to assess whether it’s likely that your customer is a late payer, how they will react to price changes or to promotions. We can also help you identify potential churners so that you can design the strategies to prevent their loss.
If you are contemplating a data science project, our consultants can join your team to help you weigh all the pros and cons of this initiative or deliver the how-to-start guidelines.
We can also be your think tank if you’ve already started but encountered some problems. For example, if your forecasting model delivers unreliable predictions, we can help you figure out how to improve its accuracy.
Our data science team can:
- Carefully examine your objectives, review your data sources and available data.
- Experiment on data and find suitable algorithms or the architecture of the neural network that will train fast and deliver reliable results.
- Advise what data you need to look at to get extra insights and/or improve the accuracy of data experiments.
- Suggest the solution’s architecture and technologies.
- Scrutinize your model and recommend how to improve its performance.
Consulting + implementation
We are strong in data science consulting, but we are also strong in turning our professional advice into a smoothly functioning solution. If you commission us to implement your data science solution, you can be sure that each recommendation given at the consulting stage will find its way into life.
Besides, if you choose this cooperation model, you won’t have to explain your company’s specifics (business objectives, processes, etc.) and the findings of the consulting stage to yet another vendor.
If you seek for consulting at multiple or all project stages or even in several projects, this is the model you need. Our team will closely cooperate with your subject matter experts and implementation team. And besides supporting you with recommendations (as set in the one-time consulting section), we’ll also work on the model’s continuous improvement. This will increase the quality of insights and help adjust the model to the changing environment.
Challenges We Solve
Our data scientists will review your data, remove duplicates, erroneous and unreliable records. In a word, we’ll run all the required data cleaning procedures to ensure that your data is of high quality. In addition, we can advise what extra data can improve analysis accuracy.
Our data consultants have a close look at your data to identify outliers. As a next step, we differentiate between signals and noise. For that, we may need expanding the data set and analyzing additional parameters, as well as consulting subject matter experts. Then we clear your data from unwanted noise, which ultimately improves the accuracy of your model.
Unstable accuracy of predictions
The reasons for such instability can vary. We will scrutinize your as-is situation to find out what causes the faulty predictions. Say, deep learning is applied, and your model suffers overfitting, meaning that it provides super-accurate predictions on the training data, but fails to work properly on real data sets. In this case, a dropout may be the way out, as it would allow breaking happenstance correlations in the training data.
Methods and Technologies
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.
Non-NN machine learning methods
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.