Role:

In your role of a Machine Learning Engineer you should be equally interested in Data Science as well as in Software Development. For any applications that have ML components your key focus will be to cover functionalities that involve building, maintaining and implementing ML models and pipelines. Your responsibilities will include: 

• working in Agile Software Development model;
• implementing of ML components, pipelines and functionalities (predominantly in Python) within the applications/systems;
• cooperation with software development teams;
• participating in product development cycle activities: defect management, resolution process, GIT repository maintenance, new feature design and implementation;
• maintaining documentation (technical descriptions, user guides, manuals, etc.);
• collaborating with PwC industry experts.

It is expected that you will grow professional expertise through seeking knowledge, skills and hands-on experience in both Software Development as well as Machine Learning (through on the job learning, external events, conferences and training) and share it with junior staff.

Candidate’s profile:

• At least 2 years of relevant professional experience;
• Hands-on industry experience in different machine learning disciplines e.g. segmentation, anomaly detection, sales prediction, text classification, sentiment detection;
• Practical knowledge of machine learning and data science concepts and solutions;
• Practical knowledge of Python for the purpose of using ML frameworks & algorithms;
• Practical knowledge of SQL in order to effectively use database engines for structured data;
• Practical knowledge of Docker for web app containerization;
• Skills in problem identification and solving, can-do attitude to overcoming technical challenges;
• Good command of the English language;
• Preferred academic background: Mathematics, Computer Science, Physics, Operational Research or related;
• Strong interpersonal and communication skills — essential in day-to-day cooperation with clients and non-technical teams.

Additional assets will be:

• Experience in programming web applications;
• The ability to use the Kubernetes container orchestration system;
• Knowledge of version control systems, design patterns and their practical use;
• Knowledge of Big Data Ecosystem: Hadoop (HDFS, YARN), Spark/Databricks (PySpark/Scala, MLlib, GraphX), Hive, Dremio;
• Experience in Java programming (with emphasis on Spring, Hibernate).

Our offer:

• Working in an international team;
• Flexible working hours;
• A broad offer of technical trainings and conferences;
• Subsidized language courses;
• Dynamic, project driven work environment;
• Excellent working conditions and friendly working atmosphere;
• Attractive compensation with additional benefits package (including copyrights).