Senior Data Scientist

Symbotic•Wilmington, MA

1d•$125,000 - $171,600•Hybrid

About The Position

With its A.I.-powered robotic technology platform, Symbotic is changing the way consumer goods move through the supply chain. Intelligent software orchestrates advanced robots in a high-density, end-to-end system – reinventing warehouse automation for increased efficiency, speed and flexibility. We are seeking a self-driven, analytical, and strategic Senior Data Scientist to serve as a foundational individual contributor within our warehouse automation organization. This role is highly impactful and hands-on. You will own deep performance analysis across our rapidly scaling automation systems and partner closely with engineering, operations, software, and leadership to uncover patterns, drive improvements, and scale performance monitoring from one site to many. You will operate as a technical leader—shaping analytics frameworks, influencing system design, and translating complex operational data into actionable insight. The ideal candidate thrives in ambiguity, enjoys working across data and engineering boundaries, and brings strong technical depth paired with clear business communication. You’ll apply advanced analytics (and AI/ML where appropriate) to move beyond surface-level metrics and expose hidden inefficiencies that materially impact system performance.

Requirements

Bachelor’s degree or higher in Data Science, Engineering, Applied Mathematics, or a related technical field.
Minimum of 8 years of experience in data analytics, performance engineering, or automation systems analysis—ideally in logistics, manufacturing, or warehousing environments.
Strong proficiency with SQL and Python (or similar), with experience using platforms such as Snowflake, BigQuery, or equivalent.
Experience with data visualization tools such as Tableau, or Grafana.
Solid understanding of microservices and event-driven architectures (e.g., Kafka, RabbitMQ) and extracting insight from distributed systems.
Proven ability to operate independently in ambiguous, data-scarce environments and build analytical structure from scratch.
Strong written and verbal communication skills, with experience presenting complex insights to senior technical and operational audiences.
Employees must have a valid driver’s license and the ability to drive and/or fly to client and other customer locations.
The employee is responsible for owning a credit card and managing expenses personally to be reimbursed on a bi-weekly basis.

Nice To Haves

Exposure to structured logging and telemetry pipelines (e.g., ELK, Loki, Prometheus).
Experience applying AI/ML techniques for predictive analytics, anomaly detection, or system modeling (e.g., scikit-learn, TensorFlow, Azure ML).
Understanding of queuing theory, simulation modeling, or performance benchmarking in automated environments.

Responsibilities

Independently analyze and model performance across robotic and non-robotic automation systems (e.g., conveyors, sorters, robotic arms, WES/WCS software).
Dive deeply into data to uncover inefficiencies, bottlenecks, and root causes—going well beyond standard dashboards.
Identify gaps in telemetry and partner with software teams to define new data contracts and instrumentation within a microservice-based, event-driven architecture.
Design and implement foundational data models and KPIs for automation system performance.
Build dashboards, visualizations, and analytical tools that clearly communicate trends, patterns, and performance drivers.
Translate complex analytical findings into clear recommendations for engineering, operations, and leadership.
Collaborate closely with engineering teams to influence automation behavior, system rules, and architectural decisions based on analytical insights.
Establish scalable frameworks for performance monitoring, data governance, and cross-site analytics.
Apply advanced analytics or AI/ML techniques where appropriate to identify leading indicators of degradation, bottlenecks, or systemic inefficiencies.
Launch repeatable analysis processes across multiple warehouse deployments.
Maintain a regular communication cadence with stakeholders to ensure insights translate into operational improvements.
Act as a subject-matter expert in automation performance analytics, mentoring peers informally and raising analytical standards across the organization.