Amylyx has an audacious mission to usher in a new era for treating diseases with high unmet needs. Where others see challenges, we see opportunities that we pursue with urgency, rigorous science, and unwavering commitment to the communities we serve. We are a clinical-stage company currently focused on post-bariatric hypoglycemia (PBH), Wolfram syndrome, and amyotrophic lateral sclerosis (ALS).

Our mission is powered by our people. Our core values – be audacious, be curious, be authentic, be engaged, and be accountable – creating a culture of caring. Amylyx has assembled an experienced team ready to take action because the communities we serve have no time to wait. If you share our passion and are determined to tackle some of medicine’s toughest problems, we encourage you to read the opportunity below and apply.

The Opportunity

Reporting to the Director, Field Operations & Data Management, the Senior Manager, Field Operations & Customer Data Management is a hands-on, execution-focused role within Commercial Operations. This position will support the reliable movement and use of customer and field data across the commercial data ecosystem, including CRM, specialty pharmacy, claims, hub, and other third-party data sources. The Senior Manager will work closely with data aggregators, source-data vendors, Information Technology, analytics partners, and field stakeholders to ensure data is delivered accurately, consistently, and on schedule into Amylyx data warehouses and downstream reporting solutions.

This role is highly tactical and detail oriented. The successful candidate will be comfortable working in the data, investigating issues, coordinating fixes, validating outputs, documenting processes, and resolving the daily operational challenges that can affect field execution. The role will also provide substantial support for customer targeting, field force sizing, territory alignment, roster and assignment maintenance, and the evaluation of new commercial data sources.

Responsibilities

Customer data operations: Support the day-to-day receipt, processing, integration, validation, and availability of customer and commercial data, including CRM, specialty pharmacy, claims, affiliations, customer reference, field activity, and other vendor-supplied datasets.
Data quality and issue resolution: Monitor recurring data deliveries and warehouse outputs; identify missing, late, incomplete, duplicated, or inconsistent data; conduct root-cause analysis; coordinate corrective actions; and follow issues through resolution.
Vendor management: Serve as an operational point of contact for data and field operations vendors, including possible data aggregator. Manage delivery calendars, file specifications, data transfer dependencies, service expectations, issue logs, status updates, and follow-up actions.
Data validation and reconciliation: Perform detailed quality control checks and reconciliations across source files, staging layers, warehouse tables, CRM outputs, vendor reports, and downstream dashboards; document findings and escalate material risks. Ensure quality data is available for incentive compensation administration.
Data documentation and controls: Maintain business rules, source-to-target mappings, data dictionaries, file layouts, standard operating procedures, validation checklists, issue histories, and other documentation needed for repeatable and auditable operations.
New data source evaluation: Support the assessment of potential new data sources by documenting business use cases, reviewing coverage and field definitions, evaluating quality and usability, coordinating sample-data testing, identifying integration requirements, and summarizing findings and tradeoffs.
Stakeholder needs and data recommendations: Engage field teams, field leadership, Commercial leadership, Analytics, and other stakeholders to clarify business questions and operational needs. Translate requests into defined data requirements and recommend the data sources, measures, analyses, or reporting approaches best suited to answer the question, while clearly communicating data limitations and tradeoffs.