Senior Data Scientist

About AESC

AESC is a global leader in the development and manufacturing of high-performance batteries for zero-emission electric vehicles(EV) and energy storage systems(ESS).

Founded in Japan in 2007 and headquartered in Yokohama, AESC has built a strong global manufacturing footprint over the past 15 years to serve key markets. The company currently operates gigafactories across Japan, the United States, the United Kingdom, France, Spain, and China. AESC has been consistently recognized by leading battery research institutions as a Global Tier 1 Battery Manufacturer. In the energy storage sector, AESC ranked among the Top 3 globally in energy storage cell shipments in 2024, according to authoritative industry sources. Led by a diverse and experienced management team, AESC employs more than 14,000 professionals worldwide. The company combines state-of-the-art technology, Japanese craftsmanship, and a strong track record in safety to deliver advanced battery solutions. AESC offers a comprehensive portfolio across various cathode chemistries and form factors, including LFP, NCM/MCA, and pouch, cylindrical, and prismatic cell formats. AESC is a trusted partner to leading global automotive manufacturers and renewable energy developers, including BMW, Mercedes-Benz, Nissan, Renault, Envision Energy, Fluence and Nidec. To date, AESC’s battery technology has powered more than one million electric vehicles and delivered over 50GWh of installed capacity in energy storage systems in over 60 countries.

Role Overview

We are seeking a Senior Data Scientist to apply advanced analytics, machine learning, and language models to optimize processes, operations, safety, and decision-making across AESC’s global energy storage business.

The focus of this role is not low-level control systems, but rather system-level intelligence and process optimization across the full BESS lifecycle, including:

• Manufacturing and quality processes
• Operations and maintenance workflows
• Safety, reliability, and risk management
• Planning, diagnostics, and decision support
• Knowledge automation and engineering productivity

This role will leverage large-scale operational, manufacturing, and engineering datasets, as well as unstructured data (documents, logs, procedures, tickets), using ML and language models to improve efficiency, consistency, and outcomes across the organization and its deployed assets.

Location: Open, Europe is a plus

*A take-home assessment that takes around 3-4 hours is required if your resume passes initial screening

Key Responsibilities

Process & Operations Optimization

• Develop data-driven models to optimize operational and maintenance processes, including:
• Failure detection and root-cause analysis
• Maintenance planning and prioritization
• Asset availability and reliability improvement
• Identify inefficiencies and variability in technical and operational workflows and propose AI-enabled improvements

Safety, Reliability & Risk Analytics

• Build models to:
• Detect early safety and reliability risks
• Analyze incident, alarm, and event data
• Support predictive and preventative risk management
• Quantify uncertainty and risk to support engineering and operational decision-making

Language Models & Knowledge Automation

• Apply large language models (LLMs) to:
• Automate and standardize engineering and operational processes
• Extract insights from unstructured data (reports, logs, procedures, contracts, standards)
• Improve knowledge retrieval, decision consistency, and response time
• Design AI tools that support:
• Engineering teams
• Operations and service teams
• Commercial and proposal teams

Data Products & Decision Support

• Design and deliver data products that provide:
• Actionable insights rather than dashboards
• Clear recommendations tied to business outcomes
• Translate complex analytical outputs into clear narratives for technical and non-technical stakeholders

Advanced Analytics & Forecasting

• Develop short-term and medium-term forecasting models for:
• Asset behavior and performance
• Operational demand and resource planning
• Support scenario analysis and “what-if” evaluations for planning and optimisation

Collaboration & Deployment

• Work closely with:
• Engineering, manufacturing, and operations teams
• Software and digital platform teams
• Ensure models and tools are deployable, maintainable, and scalable
• Monitor deployed model performance and continuously improve outcomes

Required Qualifications & Experience

Education & Experience

• Advanced degree (MSc or PhD preferred) in:
• Data Science
• Applied Mathematics
• Computer Science
• Electrical / Energy Engineering
• Physics
• 5+ years’ experience applying ML/AI to real-world systems
• Demonstrated delivery of models

Technical Skills (Must-Have)

• Strong programming skills in Python (mandatory)
• Experience with:
• Time-series analysis and forecasting
• Statistical modelling and ML algorithms
• Model validation and performance monitoring
• Experience working with large datasets and distributed systems
• Solid understanding of model lifecycle management

Nice-to-Have

• Experience with:
• PyTorch / TensorFlow
• Optimization solvers
• Digital twins or physics-informed ML
• Knowledge of DevOps, APIs, and cloud platforms
• Exposure to forecasting, bidding, or operational optimization in energy markets

Personal Attributes

• Comfortable working across software, hardware, and power-system teams
• Strong analytical and systems-thinking mindset
• Able to explain complex models to non-data-scientists
• Curious, pragmatic, and impact-focused