Own E2E data science use cases: Translate business challenges into clear analytical problems, design solution approaches, and deliver production-ready ML systems that scale.
Build and deploy advanced ML/AI solutions: Work on forecasting, segmentation, propensity, and optimization problems. Focus on solutions that are simple, effective, and scalable. Use more advanced techniques when they clearly add value.
Drive measurable business impact: Align with Global and implement robust experimentation frameworks (A/B testing, pre-post) to quantify impact. Connect model outputs to clear business KPIs like revenue uplift and cost savings.
Maintain and improve existing ML solutions: Monitor performance, fix issues, and continuously improve models in production. Keep documentation clear so others can understand and build on your work.
Collaborate with Data Engineers to build scalable pipelines using cloud platforms (Azure/Databricks), PySpark, and CI/CD workflows. Ensure models are reliable, maintainable, and continuously improved in production.
Collaborate closely with stakeholders and turn insights into clear recommendations and actions that support decision-making.

3+ years in Data Science, Machine Learning, or Advanced Analytics
Experience applying data science to solve real business problems
Experience working with stakeholders to shape problems and deliver usable solutions
Strong foundation in machine learning and statistics
Proficient in Python and common ML libraries
Comfortable working with data using SQL and/or PySpark
Experience with cloud platforms (Azure, AWS, or GCP) or similar environments
Familiarity with model deployment or experimentation methods is a plus

Data Analytics Consultant (Data Science)

Job Description