Observability, Data Scientist

Salary Range: PLN 350,700 - 474,400 + Benefits + Equity

Subject to alignment to the responsibilities and duties of the role.

Location: Gdańsk - Hybrid Working Policy - 2-3 Days per week in office

About Graphcore

At Graphcore, we’re building the future of AI compute.We’re a team of semiconductor, software and AI experts, with deep experience in creating the complete AI compute stack - from silicon and software to infrastructure at datacenter scale.As part of the SoftBank Group, backed by significant long-term investment, we are delivering key technology into the fast-growing SoftBank AI ecosystem.To meet the vast and exciting AI opportunity, Graphcore is expanding its teams around the world.We are bringing together the brightest minds to solve the toughest problems, in a place where everyone has the opportunity to make an impact on the company, our products and the future of artificial intelligence.

Job Summary We are seeking a Data Scientist / Data Analyst to transform large-scale infrastructure and hardware-level telemetry into actionable insights, predictive intelligence, and automated decision systems.

Working closely with telemetry platform engineers, you will analyze data across the full stack - from data center infrastructure down to chip-level signals (power, thermals, performance counters, reliability indicators) to detect anomalies, predict failures, and optimize system behaviour. This role complements telemetry engineering by extracting intelligence from observability systems and enabling data-driven control loops.

You will operate at the intersection of data science, distributed systems, and infrastructure observability.

Key Responsibilities

Data Analysis & Modelling

• Analyse large-scale telemetry datasets (metrics, logs, events) across multiple layers: data center (clusters, networking, cooling), system (servers, accelerators), silicon-level (on-chip sensors, performance counters, voltage, thermal, error signals).
• Develop anomaly detection models for infrastructure-level events and hardware/silicon anomalies (e.g., thermal hotspots, voltage instability, error rate drift).
• Build predictive models for fault detection and failure forecasting (e.g., hardware degradation, thermal issues, network anomalies).
• Apply statistical and machine learning techniques to identify patterns and root causes.

Automation & Intelligence

• Design automated remediation strategies informed by both system and hardware-level signals.
• Collaborate with engineering teams to integrate models into observability and control systems.
• Enable closed-loop optimization using real-time hardware telemetry streams.

Data Engineering Collaboration

• Work with telemetry engineers on data ingestion pipelines, ensuring data quality and usability.
• Help define schemas, feature extraction pipelines, and aggregation strategies (e.