Sr. Data Analyst

Neurocrine Biosciences•San Diego, CA

2d•$97,000 - $133,000

About The Position

Serves as a strategic analytics partner to Neurocrine Biosciences’ Technical Operations organization, delivering data, reporting, and business intelligence solutions across supply planning, manufacturing, logistics, and related operational functions. Independently leads analytics initiatives and contributes subject-matter expertise to complex cross-functional programs by integrating operational data, developing scalable analyses and reporting solutions, and translating findings into clear recommendations. Partners with stakeholders to establish trusted performance metrics, improve data accessibility and governance, and enable timely, data-driven decisions that strengthen operational performance, supply reliability, and continuous improvement. Neurocrine Biosciences is a biopharmaceutical company focused on discovering, developing, and commercializing treatments for neurological, psychiatric, endocrine, and immunological disorders. The company’s portfolio includes FDA-approved treatments and a pipeline of compounds in mid- to late-phase clinical development.

Requirements

Bachelor’s degree in Computer Science, Statistics, Data Science, Information Systems, or a relevant field and 4+ years of relevant experience, or a master’s degree in a related quantitative or technical field and 2+ years of relevant experience.
Relevant experience in data analytics, business intelligence, or a related role within the pharmaceutical, biotechnology, or another relevant industry.
Proven experience delivering data analytics, business intelligence, or decision-support solutions in Technical Operations, supply chain, manufacturing, or a comparable operational environment.
Advanced SQL skills, including querying, data transformation, validation, and performance-conscious development across complex datasets.
Proficiency with Python or a similar analytical programming language for data preparation, automation, statistical analysis, and reproducible workflows.
Working knowledge of data modeling, ETL/ELT processes, data warehouses or lakehouse environments, APIs, and modern data architecture concepts.
Demonstrated ability to define KPIs, develop dashboards, analyze operational performance, and convert findings into actionable recommendations.
Experience with data quality controls, metric governance, requirements gathering, user acceptance testing, documentation, and solution deployment.
Business acumen and understanding of how Technical Operations capabilities and performance support company objectives, patients, and customers.
Ability to work independently on complex assignments, exercise sound judgment, and escalate risks, decisions, or resource constraints when appropriate.
Ability to build cross-functional partnerships and translate stakeholder needs into practical, prioritized analytics solutions.
Strong analytical reasoning, structured problem-solving, and attention to detail when addressing ambiguous operational questions.
Ability to communicate complex data and technical concepts clearly to technical and nontechnical audiences through concise written, verbal, and visual presentations.
Ability to manage multiple initiatives, priorities, and deadlines while maintaining quality, transparency, and stakeholder alignment.
Commitment to data accuracy, documentation, reproducibility, and continuous improvement.
Ability to influence adoption of new metrics, tools, and processes through consultation, training, and change support.
Ability to work both independently and as a collaborative cross-functional team member.

Nice To Haves

Experience with Databricks or comparable cloud analytics platforms.

Responsibilities

Lead end-to-end analytics initiatives for Technical Operations, translating business questions into requirements, analytical approaches, deliverables, and measurable outcomes.
Integrate and analyze data from planning, manufacturing, logistics, quality, and enterprise systems to identify trends, constraints, risks, root causes, and improvement opportunities.
Develop and maintain scalable data models, datasets, dashboards, and self-service reporting solutions for accurate, timely visibility into operational performance.
Define, document, and govern key performance indicators and metric logic, aligning stakeholders on definitions, data sources, calculations, and reporting standards.
Build analyses and decision-support tools for operational planning, capacity and resource decisions, inventory and supply performance, service levels, and continuous improvement initiatives.
Partner with business and technical teams to assess data quality, resolve discrepancies, strengthen controls, and improve operational data completeness, consistency, traceability, and reliability.
Automate recurring data preparation, validation, reporting, and distribution processes to reduce manual effort, improve scalability, and accelerate access to insights.
Synthesize complex analyses into concise findings, recommendations, and executive-ready visualizations, clearly communicating assumptions, limitations, risks, and business implications.
Manage analytics workstreams and smaller projects, coordinating priorities, dependencies, timelines, testing, implementation, and stakeholder adoption.
Serve as a subject-matter resource for analytics platforms and methods by developing documentation, training users, and promoting self-service analytics and data literacy.
Ensure analytics solutions and data-handling practices comply with applicable privacy, security, governance, and records requirements; perform other duties as assigned.