Data Scientist

The Data Scientist role applies advanced analytics, statistical modeling, machine learning, and artificial intelligence to improve decision-making, operational efficiency, customer experience, and business performance. This position offers the opportunity to work with a modern cloud-based ecosystem, artificial intelligence, machine learning, and advanced analytics while contributing to BDI's continued data and AI transformation. The ideal candidate combines strong technical expertise with business acumen and can translate complex findings into recommendations that generate measurable outcomes.

Data Science and Advanced Analytics:
• Develop descriptive, predictive, and prescriptive analytical models.
• Build, validate, deploy, monitor, and continuously improve machine learning solutions.
• Apply statistical analysis, hypothesis testing, and experimentation to solve business problems.
• Identify trends, risks, opportunities, and actionable insights from enterprise data.
• Support the design and implementation of artificial intelligence and Generative AI use cases.
Business Collaboration and Value Realization
• Partner with business leaders to understand strategic priorities, operational challenges, and high-value opportunities.
• Translate business needs into analytical approaches, models, and data science solutions.
• Present findings and recommendations clearly to technical and non-technical audiences.
• Define and track measurable value, including operational efficiency, cost reduction, revenue opportunities, improved customer outcomes, and risk mitigation.

Data and Technology:
• Extract, prepare, analyze, and validate data from multiple enterprise sources.
• Collaborate with Data Engineers, Data Stewards, and Analysts to ensure data readiness and appropriate use.
• Use Snowflake as the enterprise data warehouse and leverage Azure AI Services and cloud analytics platforms to develop scalable solutions.
• Create visualizations, reports, or dashboards using Power BI when appropriate.
• Document methodologies, assumptions, code, models, dependencies, and analytical processes.

Governance and Best Practices:

• Adhere to enterprise data governance, security, privacy, and model governance standards.
• Promote responsible, ethical, transparent, and compliant use of data and AI.
• Monitor model performance and support appropriate validation, documentation, and lifecycle management.