Data Intern

Legend Biotech is a global biotechnology company dedicated to treating and, one day, curing life-threatening diseases. Headquartered in Bridgewater, New Jersey, we are advancing the next generation of cell therapy through a portfolio of innovative technologies, including autologous, allogeneic, and in vivo cell therapy approaches. Our research spans chimeric antigen receptor T-cell (CAR-T), T-cell receptor (TCR-T), natural killer (NK) cell-based therapies, and other emerging platforms designed to transform the treatment of serious diseases. From our global network of research and development sites, we are committed to discovering and developing safe, effective, and cutting-edge therapies for patients worldwide. Legend Biotech entered into a global collaboration agreement with Johnson & Johnson to jointly develop and commercialize CARVYKTI® (ciltacabtagene autoleucel; cilta-cel). Through this strategic partnership, we combine complementary expertise and capabilities to advance innovative immunotherapies for patients living with multiple myeloma. Legend Biotech is seeking a Data Intern as part of the Information Technology team based in Bridgewater, NJ. Role Overview The Data Intern will support the Data & Analytics team in collecting, organizing, analyzing, and visualizing data to enable business decision-making. The intern will gain hands-on experience with enterprise data, business intelligence tools, cloud data platforms, data quality, and documentation within a regulated life sciences environment. Working with IT, business, and data teams, the intern will contribute to reports, dashboards, data processes, analytics, automation, and AI-enabled initiatives. This opportunity is well suited for a student interested in gaining practical experience in data analytics, data engineering, and business intelligence. Key Responsibilities Assist with collecting, cleaning, transforming, and analyzing structured and unstructured data from multiple business systems. Perform exploratory data analysis to identify trends, patterns, anomalies, and potential data quality issues. Develop and enhance dashboards, reports, and visualizations using tools such as Microsoft Power BI. Write SQL queries to retrieve, analyze, and validate data from databases and cloud data platforms. Support data engineering activities, including data ingestion, transformation, validation, and reconciliation. Assist with preparing analytics-ready datasets and support the development of data and semantic models. Support enterprise data platform initiatives using technologies such as Snowflake, Microsoft Azure, Microsoft Fabric, or similar cloud platforms. Assist with documenting data sources, transformations, lineage, metadata, and business rules. Perform data quality checks and support the resolution of data inconsistencies. Support discussions with business stakeholders to understand and document reporting and analytics requirements. Assist with analytics, automation, and AI-enabled solutions where appropriate. Participate in testing and validation of reports, dashboards, datasets, and data pipelines. Prepare technical documentation, presentations, and analytical summaries. Follow organizational requirements for data privacy, cybersecurity, data governance, and the appropriate handling of confidential information. Perform other data and analytics-related activities as assigned. The Data Intern works under the guidance of the Data & Analytics team and may independently perform assigned analysis, prepare reports, document findings, and identify data quality issues. The role may recommend process improvements and highlight risks or issues but does not have authority to make architectural, compliance, financial, or strategic decisions without management approval. Requirements Currently pursuing a Bachelor's or Master's degree in: Data Science Computer Science Information Systems Statistics Mathematics Engineering Bioinformatics Business Analytics Ability to break down problems and use data to develop insights. Desire to learn new data, cloud, analytics, and AI technologies. Attention to accuracy, completeness, consistency, and traceability of data. · <span style="fo