Data Scientist

Role Summary

Data Scientist to design, implement, and deploy event-driven ETL data pipelines that ingest and process structured and unstructured laboratory records into Takeda’s data architecture, enabling efficient use of data and information. The role leverages modern data platforms, best practices, and predictive modeling to analyze laboratory methods and characterization data and unlock actionable insights.

Responsibilities

• Develop and maintain Analytical Development (AD) digital transformation road map. Deliver established vision, roadmap, projects, and timeline to support the digital transformation of analytical laboratory results.
• Support large database activities, design and maintain open-source code extraction algorithms.
• Assume technical ownership on digital transformation projects, which include but are not limited to data analytics and predictive modeling, dashboards using Spotfire, Tableau, Power BI, and open-sourced coding (i.e., R and Python).
• Develop and maintain core software engineering infrastructure within data platform engineering & operations, including data wrangling, data logging, performance benchmarking, and multi-platform integration.
• Contribute to multiple software engineering efforts, including data engineering/analytic pipelines, and data platforms.
• Coordinate with other development teams to promote collaborative data management efforts, interoperability, and shared infrastructure.
• Monitor data capture systems to identify inefficiencies and remove bottlenecks.
• Support R&D Digital, Data & Technology projects, working closely with domain experts and internal/external partners to mature POCs into production-quality tools.

Qualifications

• Required: B.S. degree in computer science, software engineering, or a related field with 8+ years of relevant industry experience; M.S. with 6+ years of relevant industry experience.
• Combined experience in the analytical environment utilizing SQL, Cloud environments, AWS, Python, Dev-Ops and R Studio.
• Experience with ETL pipelines, data pre-processing, and statistical concepts and can work in multiple languages (Python, R, Scala, C/C++, SQL).
• Exposure to cloud computing environments and technologies in the data & analytics engineering domain (Spark, Databricks, data lakes, data QA tools, ML tools) are a plus but not required.
• Strong understanding of analytical software (Spotfire, Minitab, JMP, MATLAB) and demonstrated experience contributing to and maintaining multi-contributor software projects (open or closed source), pipelines, and/or enterprise systems required.
• Previous direct involvement in technology implementations for use in biologics, pharmaceuticals or devices, either in manufacturing operations or support function is highly desirable.
• Interacts with vendors of applications, integrators and consultants during front end studies, design workshops and system implementation.
• Will need to be able to collaborate with other coworkers and work with multi-functional teams of developers, engineers, and scientists.

Skills

• SQL
• Python
• R
• Scala/C/C++
• Spotfire, Tableau, Power BI
• Databricks/Spark
• Open-source coding

Education

• B.S. in computer science, software engineering, or a related field (advanced degrees encouraged for higher levels of experience)