Data Science Engineer

As a Data Science Engineer focusing on data pipelines, you will play a critical role in developing, deploying, and optimizing cloud based solutions. This position requires experience and proficiency with Python and AWS to drive business value and enhance user experiences across our platforms.​

Key responsibilities

• Directing the data mining, data gathering, and data processing in large volume; creating appropriate data models.
• Exploring, promoting, and implementing semantic data capabilities through Natural Language Processing, text analysis and machine learning techniques.
• Defining requirements and scope of data analyses; presenting and reporting possible business insights to colleagues/management using data visualization technologies.
• Evaluating and conducting research on data model optimization and algorithms to improve effectiveness and accuracy on data analyses.
• Determining the root cause of organizational problems and creating alternative solutions that resolve these problems.

Employee perks and benefits

• 6 extra days off: 3x localhost days and 3x sick days
• Referral bonus
• Benefit plus budget
• Financial contribution to Pension plan
• Multisport Card
• Education support (certificates, courses, trainings)
• Physiotherapist sessions once a week in the office
• Bonuses at every smashing life events
• Flexible working arrangements
• Transparent approach and communication
• Supporting your ideas‍

Reyuirements for the employee

Language skills: English - Upper intermediate (B2)

Number of years of experience in the position/sector: 4

Personality requirements and skills

• Proven experience with Python (NumPy, SciPy, Pandas, etc.).
• Experience with AWS services, Cloud ELT/ETL, Snowflake, SQL.
• Working knowledge and experience in practical applications of Machine Learning techniques such as Clustering, Logistic Regression, Random Forests, SVM or Neural Networks.
• Strong knowledge of end-to-end data lifecycle across traditional data warehouses, relational databases, operational data stores, business intelligence reporting, as well as new concepts such as data fabrics, data mesh, and data lake house.
• Strong understanding of data governance, metadata management, data quality, data modeling, and data architecture concepts.
• Experience utilizing quantitative analysis/ data management (data design, data quality, metadata, governance, etc.).
• Knowledge of data technology products and components for Big Data and Cloud (AWS, Data Lakes, and similar)
• Ability to clearly communicate complex technical ideas, regardless of the technical capacity of the audience.
• Knowledge of data management systems; ability to use, support and access facilities for searching, extracting and formatting data for further use.