BI Data Scientist

• Design and maintain analytical data models to support advanced analytics, reporting, and machine learning workflows.
  
• Utilize SQL and Python to clean, process, transform, and analyze datasets efficiently.
  
• Perform exploratory data analysis to identify trends, patterns, relationships, anomalies, and areas for further investigation.
  
• Support the development and implementation of predictive and machine learning models for use cases such as forecasting, customer segmentation, behavior prediction, and other business applications.
  
• Perform data preprocessing and feature engineering to prepare high-quality datasets for machine learning and predictive modeling.
  
• Assist with the evaluation and validation of machine learning and predictive models using appropriate statistical and performance metrics.
  
• Support the integration of data science models into BI and operational workflows in collaboration with data engineering and other technical teams.
  
• Monitor model performance and contribute to the continuous improvement of predictive and analytical solutions.
  
• Develop DAX measures for business calculations, KPIs, and analytical requirements in Power BI.
  
• Create interactive dashboards and visualizations in Power BI to communicate analytical findings, model outputs, KPIs, and business insights effectively.
  
• Support the development and maintenance of ETL/ELT processes required for analytics and machine learning workflows.
  
• Conduct unit testing and validation to ensure accuracy, consistency, and reliability across analytical solutions.
  
• Follow data governance, security, privacy, and data quality standards across analytical and machine learning solutions.
  
• Collaborate with data engineering teams to improve data availability, storage, and processing solutions required for advanced analytics.
  
• Work with analysts and business stakeholders to understand business requirements and translate analytical findings into actionable insights.
  
• Create and maintain technical documentation for data models, analytical methodologies, machine learning models, queries, and related processes.
  
• Support knowledge sharing across BI teams and provide guidance to business users on analytical tools, dashboards, and data-driven decision-making.
  

Requirements

• University degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.
  
• Minimum of 1–2 years of experience in data analytics, data science, BI, or related data-driven roles.
  
• Previous experience in the FX industry is considered an advantage.
  
• Experience or exposure to data science projects involving customer analysis, behavior prediction, segmentation, forecasting, or similar predictive analytics applications.
  
• Good SQL scripting skills, including the ability to develop and optimize queries for analytical purposes.
  
• Good proficiency in Python for data analysis, machine learning, statistical modeling, and workflow automation.
  
• Good understanding of machine learning techniques and algorithms, including supervised and unsupervised learning approaches.
  
• Experience with data preprocessing, feature engineering, model evaluation, and validation.
  
• Understanding of data modeling concepts, analytical models, and data warehouse structures.
  
• Experience with Power BI for developing reports, dashboards, and data visualizations.
  
• Working knowledge of DAX for business calculations, measures, and KPIs in Power BI.
  
• Familiarity with version control systems for managing analytical code, scripts, models, and related development work.
  
• Ability to understand business requirements and translate them into analytical and data science solutions.
  
• Strong analytical, problem-solving, and critical-thinking abilities.
  
• Good communication and collaboration skills, with the ability to present analytical findings to technical and non-technical stakeholders.
  
• Excellent command of the English language.
  

• Hands-on experience developing or supporting ETL/ELT pipelines for data integration and transformation.
  
• Exposure to machine learning model deployment, monitoring, or integration into production or BI environments.
  
• Familiarity with statistical modeling, experimentation, or hypothesis testing.
  
• Knowledge of SQL Server Analysis Services (SSAS) for OLAP modeling.
  
• Exposure to Microsoft Fabric Cloud Services or Azure Data Services for cloud-based analytics and machine learning solutions.
  
• Experience working with large-scale or real-time datasets.
  
• Basic understanding of MLOps concepts, model lifecycle management, or CI/CD practices for data science solutions.
  

Benefits

• Hybrid Work Model (2 days working from home)
  
• Wolt Meal Allowance
  
• Comprehensive Health Insurance
  
• Provident Fund (Upon completion of the trial period)
  
• Summer Short Fridays (August)
  
• Additional Paid Annual Leave (up to 30 days, based on years of service)
  
• Birthday Leave
  
• Udemy Business access
  
• Gym Membership
  
• Referral Bonus Program
  

• Visa Sponsorship and Relocation Assistance