Middle Data Scientist

Job

Requirements

Bachelor’s or Master’s degree in Data

Science, Computer Science, Statistics, Mathematics, or a related quantitative

field.

2+ years of

professional experience as a Data Scientist or in a similar data-focused role.

Strong proficiency in Python and experience with data

analysis and machine learning libraries.

Strong SQL skills and hands-on experience with MySQL, PostgreSQL, and

ClickHouse.

Practical

experience working with Data Warehouses (DWH) and large-scale datasets.

Hands-on experience in developing, evaluating,

optimizing, and maintaining Machine Learning models.

Experience with ML model deployment and productionization is highly

desirable.

Experience

with Airflow for data and ML pipeline orchestration.

Experience with Git and collaborative software development

practices.

Experience

with Power BI and Tableau, including dashboard development and data visualization.

Understanding of ETL/ELT processes, data pipelines, and

data engineering concepts.

Strong analytical, problem-solving, and communication

skills.

Experience in

online payments, financial services, banking, or FinTech is an advantage.

Job

Responsibilities

Develop, evaluate, optimize, and maintain

Machine Learning models for business and product-related use cases.

Perform data analysis and exploratory data analysis to

identify trends, patterns, business opportunities, and potential risks.

Work with large datasets and complex data sources to

prepare, transform, and analyze data for analytical and ML use cases.

Design and implement data preparation and feature

engineering pipelines for Machine Learning models.

Work closely with the Data Warehouse (DWH) and analytical data infrastructure,

including ClickHouse, PostgreSQL, and MySQL.

Develop and maintain Airflow pipelines for data processing, model scoring, and ML

workflows.

Contribute

to ML model deployment and automation of model scoring and related workflows.

Monitor model performance and contribute to model

optimization, retraining, and lifecycle management.

Use Git for version control and collaborate with other team members on data and ML

projects.

Prepare and

maintain technical documentation for Machine Learning models, data pipelines, methodologies, and analytical

processes.

Build

analytical dashboards in Power BI and Tableau when required, enabling clear visualization of data, insights, trends,

and business KPIs.

Translate business requirements into data-driven solutions and analytical

models.

Collaborate

with Data Analysts, Data Engineers, Product, Business, and other stakeholders to deliver data-driven

solutions.

Continuously improve existing analytical processes, ML models, pipelines, and

reporting solutions.

Work schedule: 5 days a

week, 09:00-18:00

To apply, please send your CV to the e-mail address in the Apply for job button with the name of

the vacancy.

Applications will be evaluated based on the

requirements of the vacancy and selected candidates will be contacted.