Why does your business need data science outsourcing?
Data science outsourcing is an efficient solution that gives you access to the best professionals without the need to look for them in the market and carry the organizational burden. At Data Science UA, we gladly help you with that.
Why data science outsourcing is important:
Get your “reinforcements” in no time.
You can boost your team’s expertise and productivity very quickly. New data scientists start their work within 2 weeks after the offer;
Access to the top talents.
The search for professionals with the right skills and expertise can be time-consuming and expensive. To benefit from the best expertise in the field you might want to outsource data science services;
The recruitment process takes plenty of resources. Outsourcing data science or some other services saves your time and money;
Extra capacity and scalability.
Even if you already have data scientists, analysts, and software developers on the staff, you may still require extra resources to build something new. Besides, at some point, the company needs to scale its operations. To do it quickly you could outsource the data science part of the project;
Keeping your focus.
Outsourcing data science to some other company, allows your in-house staff to focus on the mission-critical operations of your business. Even more than that, an external team may provide an entirely new perspective for your business processes. The outsourced team will take a new look at your tools and processes, which could drive your business to the next level.
Data science employs computer science disciplines like mathematics and statistics and incorporates the following techniques:
- Data mining
- Cluster analysis
- Machine learning
What tasks can we help different industries to solve with the help of data science outsourcing?
We have a lot of experience, working with clients from various industries. Here are some examples of data science-related tasks you can outsource:
Marketing teams tend to have lots of data about advertising, web analytics, customer behavior, etc. We can fine-tune all data analysis solutions to run like clockwork and free up more of your marketing team’s time to be strategic and effective. Our team uses machine learning to:
– forecast sales;
– recommend products;
– analyze assortment and so on.
2. Retail (E-commerce)
Retail usually accumulates large amounts of data and is eager to use data analytics.
We can help with:
– customer analysis;
– assortment analysis;
– sales forecasts;
– marketing and advertising budgets optimization;
– increase the efficiency of merchandising and supply chain management.
Generation of optimized plans that enable predictive maintenance is one of the key goals for AI in manufacturing, as well it helps in:
– optimizing production lines and logistic chains;
– forecasting revenue;
– determining optimal employee workloads;
– setting up automated systems for monitoring compliance with safety regulations.
When artificial intelligence is working with IoT devices it means that data can be analyzed and decisions can be made without involvement by people. In a broad variety of industries where IoT is implemented, AI can help to identify patterns and detect anomalies in the data that smart devices and sensors transfer (for example, air quality, humidity, temperature, pressure, vibration, sound, and others).
FinTech companies usually work with sensitive information and have high-security standards. We take all necessary precautions to keep their data safe. Data Science UA can assist such businesses in:
– credit scoring;
– recommendation systems for both new and prospective clients.
6. Logistics & Warehouses
The transportation and warehouse industry is data-driven and needs analysis of historical and real-time data performed by intelligent algorithms. So our team can help with:
- traffic management improvements
- warehouse optimization,
- route optimization (“travelling salesman” problem),
- developing optimal loading systems and utilization systems for vehicles;
AI can help insurance companies deliver high-quality service as it has done for major leaders in other industries such as Healthcare, Fintech, etc.
We can help to:
- create a more personalized service;
- predict the repair costs from historical data;
- provide a selection of better investments based on risks, preferences, and spending patterns;
- improve claims analysis.
Farmers aim to maximize production and profits using innovative software and data collection and analysis. We can make the analysis of historical and real-time images & data collected from databases, satellites, drones, IoT sensors that can help to:
- increase the yield of farmlands;
- ensure serviceability of farm equipment;
- monitor fields conditions, irrigation, soil moisture, etc;
- predict weather conditions.
Nowadays AI helps to deploy effective cybersecurity technology and allows businesses to solve major cybersecurity challenges: cyberattack, financial loss, or brand reputation damage. We can help cybersecurity teams to:
- analyze patterns in user behaviors and respond to changing behavior;
- identify cyber vulnerabilities and irregularities in the network.
AI is already transforming the healthcare industry—helping patients and hospitals optimize costs and increase care delivery through actionable insights. We can help to:
- manage and analyze data to provide;
- improve preventive care;
- create personalized treatments;
- make optimization of scheduling and bed management;
- detect and analyze patient patterns and correlations for better decision making.
Founder and CEO, Elafris Inc
CEO and Founder, Reply
Michael Korkin, Ph.D.
CTO at Entropix, Inc.
Technologies we leverage
Languages: Python, R, Scala, SQL, C++, etc.
Visualization: Power BI, Tableau, Qlik, Matplotlib, seaborn, ggplot2, plotly, Bokeh
DBMS: Relational (MS SQL, PostgreSQL, MySQL), Non-relational (MongoDB, CouchDB, Cassandra etc.), Distributed (Hadoop etc.)
DL Frameworks: PyTorch, Tensorflow, Keras
Architectures: On-premise, cloud, hybrid
Algorithms: Supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction, anomaly detection, pattern search), ensembles, reinforcement learning
Fields: Natural Language Processing, Computer Vision, Recommendation systems, Tabular data analysis, Signal Processing
Neural Networks: CNN, RNN, DNN, LSTM, GAN, Autoencoders
Cloud Platforms: Amazon Web Services, Google Cloud Platform, Microsoft Azure
Why should you choose Data Science UA for data science outsourcing?
- Our monthly service fee is relatively low and in the long run, it is very cost-effective cooperation;
- We strive to make our cooperation transparent. After the initial call we send you our business proposal with all details about the salaries, costs, and detailed sample calculations;
- You can choose to work with us in team-extension-mode or on a project basis. The engineers we hire will be 100%-focused on the tasks you provide.
FAQ about Data Science Outsourcing
What exactly is data science outsourcing?
Data science outsoring is model of cooperation with experts in data science and AI, who have broad experiece and needed skills to define the suitable approach for the data modelling requirements.
You collaborate with a team of data science consultants who gather and analyze data, and make suggestions and recommendations. After that results are tested throug the implementation.
Why outsourcing data management is so popular?
Data management implementation is important as it gives your company an opportunity to make smarter business decisions. By attracting experts in data science with deep understanding of business operations across the number of industries, you can navigate risks and decrease your operational costs with effective implementation of data managment tailored to yout exact needs.
Data Science outsourcing: why is it important in business?
Data science outsoring is a cost-effective way to test your ideas and hypotheses with experts in data science and AI to create the most efficient roadmap for your data and models.