Data Scientist (She/ He/ They)

Capco Poland; Poland; Hybrid; PLN 180,000 - 300,000; Full time

Job summary

Capco Poland is seeking a data scientist to join a strategic forecasting and model development programme. You will design, develop and enhance predictive models across the full DS lifecycle, including deployment and monitoring.

The ideal candidate brings strong Python skills, time series forecasting experience, solid statistical knowledge, and hands‑on ML experience. Azure ML, Databricks, and R familiarity are highly desirable. This is a flexible B2B collaboration with diverse projects.

Qualifications

• Strong commercial experience as a Data Scientist or Machine Learning Engineer.
• Advanced proficiency in Python.
• Demonstrable experience developing Time Series Forecasting Models.
• Strong understanding of statistics, probability, and predictive modelling techniques.
• Experience with machine learning frameworks and model validation methodologies.
• Proven ability to work with large and complex datasets.
• Experience collaborating with cross‑functional business and technical teams.
• Strong communication and stakeholder management skills.

Responsibilities

• Develop, validate, and optimize advanced time series forecasting and predictive models.
• Utilize Python for data analysis, feature engineering, statistical modelling, and machine learning development.
• Build and deploy machine learning solutions using Azure Machine Learning and Databricks.
• Support and enhance existing forecasting models, including models developed in R.
• Implement and maintain MLOps processes to enable efficient model deployment, monitoring, and lifecycle management.
• Work closely with business stakeholders to understand requirements and translate them into scalable data science solutions.
• Develop and maintain robust analytical datasets and data models to support forecasting and reporting needs.
• Leverage Databricks for large-scale data processing and advanced analytics.
• Ensure data quality, integrity, and governance standards are maintained throughout the modelling lifecycle.
• Create clear documentation and communicate modelling outcomes and recommendations to technical and non‑technical audiences.
• Contribute to Agile delivery practices and continuous improvement initiatives.
• Stay current with emerging trends and best practices in data science, machine learning, cloud technologies, and analytics.

Skills

Python

Time Series Modelling

Statistics

Machine Learning

Data Analysis

Communication

Tools

Azure Machine Learning

Databricks

R programming

Job description

We are looking for Poland based candidate. At Capco Poland, we’re not just another consultancy - we’re the spark behind digital transformation in the financial world. As a global leader in technology and management consulting, we thrive on helping clients tackle the toughest challenges across banking, payments, capital markets, wealth, and asset management.

We are seeking an experienced Data Scientist with strong expertise in Python and Time Series Modelling to join a strategic forecasting and model development programme. The successful candidate will play a key role in designing, developing, and enhancing predictive models while working across the full data science lifecycle, from data exploration through to deployment and model monitoring. The ideal candidate will bring strong analytical and statistical capabilities, hands‑on machine learning experience, and the ability to collaborate effectively with both business and technical stakeholders. Experience with Azure Machine Learning, Databricks, and R programming is highly desirable.

We offer a flexible collaboration model based on a B2B contract, with the opportunity to work on diverse projects.

What You'll Deliver

• Develop, validate, and optimize advanced time series forecasting and predictive models.
• Utilize Python for data analysis, feature engineering, statistical modelling, and machine learning development.
• Build and deploy machine learning solutions using Azure Machine Learning and Databricks.
• Support and enhance existing forecasting models, including models developed in R.
• Implement and maintain MLOps processes to enable efficient model deployment, monitoring, and lifecycle management.
• Work closely with business stakeholders to understand requirements and translate them into scalable data science solutions.
• Develop and maintain robust analytical datasets and data models to support forecasting and reporting needs.
• Leverage Databricks for large-scale data processing and advanced analytics.
• Ensure data quality, integrity, and governance standards are maintained throughout the modelling lifecycle.
• Create clear documentation and communicate modelling outcomes and recommendations to technical and non‑technical audiences.
• Contribute to Agile delivery practices and continuous improvement initiatives.
• Stay current with emerging trends and best practices in data science, machine learning, cloud technologies, and analytics.

What We're Looking For

• Strong commercial experience as a Data Scientist or Machine Learning Engineer.
• Advanced proficiency in Python.
• Demonstrable experience developing Time Series Forecasting Models.
• Strong understanding of statistics, probability, and predictive modelling techniques.
• Experience with machine learning frameworks and model validation methodologies.
• Proven ability to work with large and complex datasets.
• Experience collaborating with cross‑functional business and technical teams.
• Strong communication and stakeholder management skills.

Bonus Points For

• Experience with Azure Machine Learning.
• Experience with Databricks and distributed data processing.
• Exposure to MLOps, CI/CD pipelines, and model monitoring.
• Experience with R programming, including: RStudio, tidyverse (dplyr, tidyr), caret, randomForest, xgboost, ggplot2, Shiny
• Experience developing and supporting data pipelines.
• Knowledge of Git/version control and Agile delivery methodologies. Experience working with Geospatial Data and spatial analytics.

RECRUITMENT PROCESS

• HR interview with the recruiter
• Technical interview with Capco Engineering team
• Client interview
• Feedback and offer