BI Data Scientist

• Design, develop, and optimize analytical data models to support advanced analytics, reporting, and machine learning workflows.
  
• Utilize SQL and Python to clean, process, transform, and analyze large and complex datasets efficiently.
  
• Perform exploratory data analysis to identify trends, patterns, relationships,anomalies, and areas for further investigation.
  
• Develop and implement 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.
  
• Evaluate and validate machine learning and predictive models using appropriate statistical and performance metrics to ensure accuracy, reliability, and business
  relevance.
• Support the deployment and integration of data science models into BI and operational workflows, working closely with data engineering and other technical
  teams.
  
• Monitor model performance and contribute to the continuous improvement of predictive and analytical solutions.
  
• Develop advanced DAX measures for complex 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.
  
• Develop, optimize, and maintain ETL/ELT processes where required to support analytics and machine learning workflows.
  
• Conduct unit testing and validation to ensure accuracy, consistency, performance,and reliability across analytical solutions.
  
• Ensure data integrity, consistency, scalability, and compliance with applicable data governance, security, and privacy standards.
  
• Work closely with data engineering teams to improve data architecture,availability, storage, and processing solutions required for advanced analytics.
  
• Collaborate with analysts and business stakeholders to identify opportunities,define business problems, and translate complex analytical findings into actionable strategies.
  
• 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 3 years of experience in data analytics, data science, BI, or related data-driven roles.
  
• Previous experience in the FX industry is required.
  
• Solid experience in data science projects involving customer analysis, behavior prediction, segmentation, forecasting, or similar predictive analytics applications.
  
• Proficiency in SQL scripting, including the ability to develop and optimize complex queries for performance and scalability.
  
• Strong proficiency in Python for data analysis, machine learning, statistical modeling, and workflow automation.
  
• Practical knowledge of machine learning techniques and algorithms, including supervised and unsupervised learning approaches.
  
• Experience in data preprocessing, feature engineering, model evaluation, and validation.
  
• Good understanding of data modeling concepts, analytical models, and data warehouse structures.
  
• Proficiency in Power BI for developing interactive reports, dashboards, and data visualizations.
  
• Advanced skills in DAX for developing 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 translate business requirements and challenges into analytical and data science solutions.
  
• Strong analytical, problem-solving, and critical-thinking abilities.
  
• Strong communication and collaboration skills, with the ability to present complex analytical findings to technical and non-technical stakeholders.
  
• Excellent command of the English language.
  

• Hands-on experience developing and maintaining ETL/ELT pipelines for data integration and transformation.
  
• Knowledge of machine learning model deployment, monitoring, and 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.
  
• Understanding of MLOps concepts, model lifecycle management, or CI/CD practices for data science solutions.
  
  

Benefits

• Hybrid Work Model (2 days working from home)
  
• Comprehensive Health plan starting from the first day of employment
  
• Pension plan
  
• 13th salary payment
  
• Additional Paid Annual Leave (up to 30 days, based on years of service)
  
• Up to 5 Carry over annual leave days from previous year to the next one
  
• Birthday Leave
  
• Udemy Business access
  
• Monthly Wolt Vouchers
  
• Monthly meals & treats at the office
  
• Participation in company's Group Discount Scheme
  
• Gym Membership
  
• Referral Bonus Program
  
• Summer Short Fridays (August)
  

• Visa Sponsorship and Relocation Assistance (If applicable)