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Master's thesis - Junior Data Scientist - Natural Language Processing - Job Opportunity at Siemens Energy

Karlsruhe, Germany
Temporary
Entry-level
Posted: March 7, 2025
On-site
EUR 35,000-45,000 annual equivalent for thesis position with potential for higher compensation in subsequent full-time role

Benefits

Professional development through hands-on AI/ML experience
Exposure to industry-leading energy technology projects
Integration into global R&D environment
Career foundation opportunities with industry leader
Networking opportunities within a 100,000+ employee organization

Key Responsibilities

Design and implement AI-driven knowledge expert system
Evaluate and select appropriate on-premise chat models
Conduct model training and fine-tuning with diverse data sources
Develop systematic quality assessment frameworks
Perform data collection, cleaning, and preprocessing
Execute exploratory data analysis
Document technical processes and findings

Requirements

Education

Ongoing Master's degree in Computer Science, Artificial Intelligence, Data Science, Machine Learning, or related field

Experience

0-1 years

Required Skills

Python Data science libraries (Pandas, NumPy, Scikit-learn) Machine learning concepts and algorithms Data visualization (Matplotlib, Seaborn) Natural Language Processing
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Sauge AI Market Intelligence

Industry Trends

The energy sector is experiencing rapid transformation toward AI-driven solutions, particularly in knowledge management and operational efficiency. There is growing demand for NLP specialists in industrial settings as companies seek to digitize and automate domain expertise. The convergence of energy sector expertise with AI capabilities is creating unique opportunities for specialists who can bridge both domains.

Role Significance

Likely part of a specialized AI/ML team within the Transformation of Industry division, typically 5-8 members including senior engineers and researchers
Entry-level position with direct impact on core knowledge management systems and potential to influence future AI implementation strategies

Key Projects

Development of industrial knowledge management systems Implementation of conversational AI for technical applications Domain-specific language model customization Data integration and preprocessing pipelines

Success Factors

Strong foundation in NLP techniques and implementation Ability to bridge theoretical ML knowledge with practical industrial applications Understanding of industrial domain knowledge representation Effective documentation and communication skills Adaptability to evolving AI technologies

Market Demand

High demand for NLP specialists in industrial settings, particularly in energy sector digital transformation initiatives

Important Skills

Critical Skills

NLP expertise - essential for core project objectives and knowledge system development Python programming - fundamental for implementation and integration Machine learning fundamentals - crucial for model selection and training

Beneficial Skills

Energy sector knowledge Large language model optimization Data pipeline development Technical documentation skills

Unique Aspects

Opportunity to work on cutting-edge AI applications in industrial setting
Direct contribution to energy sector transformation
Exposure to both academic research and practical implementation
Access to extensive industrial domain knowledge and data

Career Growth

6-12 months thesis duration with potential for permanent position, typical progression to mid-level roles within 2-3 years

Potential Next Roles

Data Scientist Machine Learning Engineer AI Research Engineer Technical Lead - NLP Industrial AI Specialist

Company Overview

Siemens Energy

Siemens Energy is a global leader in energy technology and infrastructure, serving as a crucial player in the energy transition movement

Major market position with significant influence in energy sector digital transformation and sustainable energy solutions
Strong presence in Germany with global reach across 90+ countries
Innovation-focused environment emphasizing sustainability, diversity, and technological advancement
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