Data Scientist - Outsourcing

Overview

We are seeking a skilled and intellectually curious Data Scientist with practical experience applying statistical, analytical, and machine learning methods to solve real‑world problems. This role requires strong technical foundations in applied statistics, predictive modeling, and data science workflows, along with the ability to translate complex findings into insights that drive business and operational decision‑making. Ideal candidates will have experience working with modern analytics tools and cloud platforms and may include recently matriculated Bachelor’s or Master’s degree candidates with meaningful hands‑on project, internship, or applied academic experience.

Note: Employment-based non-immigrant visa sponsorship and/or assistance is not offered for this specific job opportunity.

The Role

Key Responsibilities

• Apply advanced descriptive and inferential statistical methods to explore data, evaluate hypotheses, and produce actionable insights.
• Build, optimize, and interpret regression models (linear, logistic), and classification models commonly used in data science, including tree‑based and ensemble methods.
• Develop robust data pipelines and analytic workflows using R or Python, including data ingestion, cleaning, feature engineering, and model deployment support.
• Construct high‑quality data visualizations, dashboards, and analytic stories that clearly communicate trends, relationships, and operational insights to diverse audiences.
• Write and optimize SQL queries for data extraction, transformation, and validation across relational data environments.
• Implement analytics solutions within cloud environments, such as Azure services, Azure Foundry, Blob Storage, and SharePoint, to support scalable data storage, processing, and reporting.
• Build and maintain analytical assets and reusable code to streamline recurring analyses and enhance data science capability.
• Partner with product, operational, and technology teams as an informed contributor in Agile development environments, applying concepts such as MVP thinking, user stories, iterative feedback cycles, and release planning.
• Engage with operational stakeholders—including client delivery, service center operations, and benefits administration teams—to provide data‑driven perspectives that support performance improvement, quality management, and client outcomes.
• Communicate modeling approaches, assumptions, risks, and results in a clear and credible manner to both technical and non‑technical audiences.