Data Scientist

Навыки

• Agile
• Azure
• B2B-продажи
• XGBoost / LightGBM / CatBoost
• CI/CD
• Анализ данных
• Data Quality

О компании и продукте

• 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 provide data science services within a strategic forecasting and model development programme

Задачи

• CAPCO POLAND
• 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

Требования

• 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
• 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
• What You'll Deliver

Будет плюсом

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

Как проходит отбор

• HR interview with the recruiter
• Technical interview with Capco Engineering team
• Client interview
• Feedback and offer
• We have been informed of several recruitment scams targeting the public
• We strongly advise you to verify identities before engaging in recruitment related communication
• All official Capco communication will be conducted via a Capco recruiter

Паспорт вакансии

История публикации

Откуда что взялось

Отмечено то, что вывели мы. Без пометки — значение назвал работодатель.

Почему на этом месте в выдаче

Порядок выдачи объявлен контрактом: свежесть решает между днями, полнота и зарплата — внутри дня.