BI Data Scientist

HFM is an

internationally acclaimed multi-asset broker, delivering

cutting-edge trading tools, platforms, and conditions to traders

worldwide. We are committed to innovation, transparency, and

excellence in the financial markets.

As we

continue to expand, we are seeking a driven and strategic BI Data Scientist to

join our team.

Responsibilities :

• 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.

Nice

to have

:

• 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.

Resumes

must be submitted in English.

Applicants

must be eligible or have legal authorization

to work in t

he country where the position is

based.

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)

Additional

Support

• Visa
• Sponsorship and Relocation Assistance (If applicable)

4. Closing

Statement

(Mandatory – Use

the approved wording below)

Sounds

like you? Let’s write the next chapter together!

All

Applications will be handled with the strictest

confidentiality.