Data Science Trainee( ОсОО ЭПАМ Системз ) (в архиве)

26 Сентября

Партнерские Вакансии

Компания "ОсОО ЭПАМ Системз"

Striving to gain market-oriented knowledge and skills to jumpstart your career in IT? Apply for this program and shape your professional path with EPAM experts.

Details

If you are curious about how you can connect your strong math skills with programming and pave your way in the data-driven world, then this program is for you.

By participating, you will have the opportunity to:

• Gain a strong foundation in software engineering
• Dive deep into statistics and data analysis
• Learn regression, classification, and clustering techniques
• Explore the basics of deep learning and neural networks
• Join one of our mentoring programs in specialized Data Science domains

After successfully completing all program stages, you will gain market-oriented soft and hard skills, which you may further apply at EPAM or elsewhere in the IT industry.

What do we offer?

• Industry-based education. As a leading software engineering company, we will help you explore emerging technologies and best practices that the market demands.
• Top-notch learning materials. Our Data Science specialists with extensive project experience have designed and tested the educational content in numerous training runs.
• Practice-oriented approach. This comprehensive program focuses on providing you with hands-on experience and practical application of the concepts learned.
• Deep dive into the specialization. Our graduates become highly skilled specialists ready to face complex technical challenges and work with the world's leading clients.
• Support from experienced mentors. We will guide you at all training stages, covering your open questions and sharing feedback on assigned tasks.
• Career advancement. Upon successful completion of both fundamentals and specialization stages, we will consider you for open positions based on your demonstrated skills and available opportunities at EPAM.

Training process

The program is designed to guide you through two engaging stages:

Stage 1: Fundamentals (3 months with ~12 hours per week)

In this stage, you'll build a strong foundation in Data Science. Here's what to expect:

• Weekly learning. Explore video lessons and self-study materials, then practice through tasks and tests – all within set deadlines to ensure steady progress.
• Mentor guidance. Submit your weekly practical assignments for feedback and approval from expert mentors.
• Interactive support. Join weekly group Q&A sessions with professionals to discuss questions and deepen your understanding.

Perform well to pass the technical interview and advance to the next level!

Stage 2: Specialization (3.5 months with ~20 hours per week)

This stage is all about taking your skills to the advanced level with a more intensive approach. You will join one of the proposed mentoring programs:

• Computer Vision
• Natural Language Processing
• Machine Learning Engineering
• Time Series
• Recommendation Systems

By the end, you'll acquire job-ready skills to tackle real-world data challenges confidently.

What is required for training:

General requirements:

• Citizens of Kyrgyzstan and permanent residents who are eligible to work in this country
• Second-to-last or final year university students and recent graduates

Skills requirements:

• English level B2 (Upper-Intermediate) or higher
• Proficiency in mathematical analysis (main topics of derivatives, integrals, extrema of functions in multidimensional real space)
• Skills in linear algebra (vectors, matrices, tensors, linear equations, eigenvalues and eigenvectors, quadratic forms)
• Knowledge of probability theory (definition of probability, conditional probability, Bayes theorem, expectation)
• Command of statistics (basic concepts, hypothesis testing, concept of likelihood, estimation of distribution parameter)
• Understanding of basic optimization concepts and methods (stationary points, Lagrange multipliers, gradient descent)
• Knowledge of data structures and algorithms (sorting algorithms, algorithm complexity)
• Familiarity with Python fundamentals (NumPy and Pandas in particular)