Senior Data Analyst

Roche•Indianapolis, IN

13d•Onsite

About The Position

At Roche, we are passionate about transforming patients’ lives, and we are bold in both decision and action - we believe that good business means a better world. That is why we come to work every single day. We commit ourselves to scientific rigor, unassailable ethics, and access to medical innovations for all. We do this today to build a better tomorrow. Roche is strongly committed to a diverse and inclusive workplace. We strive to build teams that represent a range of backgrounds, perspectives, and skills. Embracing diversity enables us to create a great place to work and to innovate for patients. Operations Near Patient Care (DOB) is a key pillar within Global Diagnostic Operations. Thanks to the systematic improvement of our cost structure, continuous optimizations, and efficiency enhancements, we are successfully positioning ourselves for growth in our environment. The Opportunity Be part of our journey. As a Senior Data Analyst in our Near Patient Care manufacturing environment, you will develop and deliver data analytics solutions that help us understand, monitor and improve product and process performance across our portfolios, including CGM. In this role, you will act as a Data Product Owner for selected analytics solutions and data products. You will translate business and process needs into clear analytical requirements, guide end-to-end delivery from problem framing to implementation and continuous improvement, and ensure that solutions are aligned with global data, platform and governance standards. You will combine strong analytical expertise with a solid understanding of manufacturing processes, IT/OT systems and data engineering fundamentals. You will work hands-on where needed, while also providing technical guidance to colleagues and coordinating cross-functional teams to solve complex, high-impact problems.

Requirements

Bachelor degree in engineering, computer science, data science, business administration, or a related field is required.
5+ years of experience in manufacturing, production processes, data analytics, data science, business intelligence, or a closely related field.
Fluency in English is required.

Nice To Haves

Strong understanding of manufacturing and quality processes, with the ability to analyze process flows, identify performance drivers and translate operational challenges into prioritized data and analytics requirements.
Experience in owning or leading analytics solutions, data products or cross-functional data initiatives from requirements definition through implementation and continuous improvement.
Strong analytical and technical skills, including hands-on experience with data analysis, data preparation and data visualization.
Experience with SQL, Python, R or comparable tools is highly desirable.
Good understanding of data engineering fundamentals, including databases, data models, ETL/ELT concepts, data pipelines, data quality, lineage and scalable data product design.
Deep understanding of IT/OT systems, digital solutions, analytics models and business system landscapes within manufacturing, quality control, planning and related reporting frameworks.
Excellent knowledge of data analytics, data product / data mesh principles and relevant systems such as Snowflake, Tableau and other BI tools.
Understanding of data governance, data integrity, GxP / CSV-relevant documentation principles and compliance expectations in a regulated manufacturing environment is beneficial.
Proven ability to guide colleagues, coordinate work packages, influence without formal authority and lead cross-functional teams through complex analytical problem solving.
Strong decision-making ability, structured thinking and the ability to manage priorities, resources and stakeholder expectations in a complex matrix environment.
German language skills are beneficial to enable effective collaboration with local teams and international stakeholders.

Responsibilities

Own and drive selected data products and analytics solutions with focus on CGM 2.0 (DOT) across their lifecycle, from business need, requirements and prioritization to development, testing, release, usage, continuous improvement and phase-out.
Work closely with manufacturing, quality, planning and digital stakeholders to understand process challenges, translate them into data and analytics requirements, and assess data needs based on operational value, feasibility, quality impact and scalability.
Independently perform complex analyses and establish effective process data monitoring to identify performance variation, prevent incidents and support the avoidance of quality deviations.
Apply analytical thinking, data science methods and professional judgment to evaluate, select and implement approaches for predicting system and process performance.
Contribute hands-on to data preparation, data modelling, data quality checks and analytical implementation where required, and collaborate closely with data engineers and platform teams to ensure robust, scalable and maintainable solutions.
Contribute to data product governance and ensure that analytics solutions are developed in line with relevant data integrity, compliance, documentation and global platform requirements, including alignment with GO Digital 2.0 / Roche Diagnostics Data Mesh principles where applicable.
Coordinate task distribution within cross-functional analytics work packages, provide expert guidance to colleagues and act as a best-practices resource for data analytics, data product delivery and analytical problem solving.
Proactively identify sources of product and process performance variation through data analysis, experiments and close collaboration with subject matter experts, ensuring that insights are translated into future data product and process improvements.
Foster effective cooperation with manufacturing, quality, planning, IT/OT, global digital teams and other relevant interfaces to resolve delivery failures, customer-visible product performance problems and operational improvement opportunities.
Support leadership and operational teams by preparing, analyzing and communicating technical reports, performance insights and decision-relevant analytics in a clear, actionable way.