IT Data Science Engineer

Responsibilities

• Business requirements analysis: Translate business objectives, service performance questions, and stakeholder requirements into analytical use cases.
• Define success criteria: Establish analytical success criteria aligned with service and company requirements.
• Business intelligence development: Design, develop, and maintain interactive dashboards, self-service reports, SSRS reports, and SSAS cubes for operational and strategic decision-making across different teams.
• Statistical and predictive modelling: Design, build, and evaluate statistical, predictive, and machine-learning models.
• Machine learning deployment: Develop and deploy machine-learning models for customer churn prediction, revenue forecasting, customer segmentation, and anomaly detection.
• Statistical testing: Perform statistical analysis, hypothesis testing, and A/B test framework design to support product, marketing, and operational initiatives.
• Exploratory data analysis: Identify trends, risks, anomalies, and opportunities for improvement through data exploration.
• Machine-learning model maintenance: Extend and maintain existing ML models, retraining and validating them as new data becomes available.
• Data modelling: Design and maintain dimensional data models following OLAP best practices, including star schemas, snowflake schemas, and SSAS cubes.

Qualifications

• Bachelor's degree in Data Science, Computer Science, Information Technology, or a related field.
• Experience with SSRS, SSAS, and Power BI or Tableau in a production environment.
• Minimum of 3–5 years of hands-on BI and analytics development experience.
• Relevant certifications are an advantage, including: (Microsoft Power BI Data Analyst, Tableau Desktop Specialist, Google Data Analytics, - - AWS Certified Machine Learning)

Skills