Responsibilities include but not limited to:

• Conduct exploratory data analysis to identify trends, patterns, and improvement opportunities.
• Perform data cleaning and preprocessing to prepare datasets for modeling and reporting.
• Build and evaluate predictive models to support decision-making across operations.
• Create dashboards and reports for data storytelling to communicate insights to business stakeholders.
• Collaborate with cross-functional teams on data pipeline integration and analytics enablement.
• Provide data-driven recommendations that improve operational efficiency, accuracy, and business outcomes.
• Assist in analytics tasks related to system implementations, process changes, or technology upgrades.

 
Required Experience & Education 

• Master’s Degree in Data Science, Analytics, Statistics, Computer Science, or a related quantitative field preferred
•  Bachelor’s Degree in a STEM discipline with strong analytical coursework (required)
•  Coursework or academic projects in machine learning, statistics, data engineering, optimization, or operations analytics.
• Exposure to cloud computing, database systems, or business analytics is a plus.
• Capstone, thesis, or applied research demonstrating hands-on data work is strongly preferred.

 Required Knowledge & Skills 

• Curiosity & Learning Mindset — eagerness to explore new tools, methods, and business domains.
• Communication — explaining technical concepts to non-technical stakeholders.
• Analytical Thinking — ability to break down complex problems into structured analytical steps.
•  Machine Learning Fundamentals — regression, classification, clustering, model evaluation, and basic time-series concepts.
• Database Concepts — relational databases, SQL querying, joins, indexing, and basic data modelling.
•  Cloud & Modern Data Platforms — familiarity with AWS, Azure, or GCP services for analytics
• Business & Operational Understanding — ability to interpret data in the context of business processes, KPIs, and operational goals.