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Data Scientist - Job Opportunity at QAD, Inc.

Paris, France
Full-time
Mid-level
Posted: June 10, 2025
Remote
EUR 65,000 - 85,000 annually based on mid-level experience requirements, Paris market conditions, and the specialized nature of LLM/RAG expertise. The role's focus on cutting-edge AI technologies and the company's SaaS growth trajectory suggest compensation in the upper range of this estimate, potentially reaching EUR 90,000+ with performance incentives.

Benefits

Virtual-first work environment providing exceptional work-life balance and flexibility in a competitive market where remote work is increasingly valued
Comprehensive professional development opportunities through exposure to cutting-edge AI/ML technologies and cross-functional collaboration
International exposure working with diverse US and European engineering teams, enhancing global perspective and networking opportunities
Travel allowances for office visits to strengthen professional relationships and strategic alignment
Access to latest AI research and internal knowledge-sharing initiatives for continuous learning

Key Responsibilities

Lead the development and optimization of advanced Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems that directly impact product innovation and competitive advantage
Architect and implement AI Agents with decision-making capabilities, positioning the organization at the forefront of autonomous AI technology
Drive research initiatives in prompt engineering techniques to maximize model performance, contributing to measurable improvements in system accuracy and efficiency
Design and maintain production-grade machine learning pipelines that ensure scalable AI/ML model deployment and operational excellence
Process and analyze complex multimodal datasets including text and images, enabling comprehensive data-driven insights for strategic decision making
Collaborate strategically with engineering teams to integrate AI models into scalable applications, ensuring seamless product enhancement and user experience optimization
Conduct advanced analysis of model outputs to refine algorithms and drive continuous performance improvements that align with business objectives
Champion knowledge transfer initiatives and stay current with AI/ML research trends, establishing the organization as a thought leader in the industry

Requirements

Education

Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or a related field

Experience

3+ years of professional experience

Required Skills

Strong programming skills in Python and familiarity with libraries such as TensorFlow, PyTorch, LangChain, and Hugging Face Transformers Experience with NLP, LLM fine-tuning, and retrieval-based systems Knowledge of prompt engineering techniques, LLM jailbreak methods and AI Agents Understanding of traditional and modern machine learning algorithms, including deep learning architectures Familiarity with cloud platforms such as AWS, GCP, or Azure for AI/ML deployments Basic understanding of data engineering, ETL pipelines, and model deployment Good collaboration skills at all levels with cross-functional teams Highly developed ownership and creative thinking Analytical thinking and the ability to solve complex problems Process orientation and ability to build effective solutions Time management and organizational skills Fluent English language skills
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Sauge AI Market Intelligence

Industry Trends

The manufacturing and supply chain technology sector is experiencing unprecedented transformation through AI integration, with companies investing heavily in intelligent automation and predictive analytics to maintain competitive advantage. Organizations are particularly focused on implementing RAG systems and LLMs to enhance decision-making processes and operational efficiency. Generative AI and Large Language Model adoption in enterprise software has accelerated dramatically, with manufacturing companies seeking to leverage these technologies for supply chain optimization, predictive maintenance, and intelligent resource allocation. The demand for professionals who can bridge traditional manufacturing domain knowledge with cutting-edge AI capabilities is at an all-time high. The shift toward virtual-first work environments in tech companies has become permanent, with organizations recognizing the talent acquisition advantages and cost efficiencies. This trend is particularly pronounced in AI/ML roles where global talent pools are essential for accessing specialized skills in emerging technologies like prompt engineering and AI agents.

Role Significance

Likely part of a 15-25 person engineering organization spanning US and Europe, with direct collaboration with 3-5 data engineers and software engineers. The role probably involves mentoring junior team members while reporting to a senior data science manager or AI/ML director.
This mid-level position carries significant strategic importance as it sits at the intersection of product innovation and AI research. The role involves independent decision-making on model architecture and algorithm optimization, with direct impact on product capabilities and competitive positioning. The responsibility for maintaining production ML pipelines and collaborating across international teams indicates substantial operational influence.

Key Projects

Development of intelligent manufacturing optimization systems using RAG architectures to process complex supply chain documentation and historical data Implementation of conversational AI agents for customer support and internal operations, leveraging advanced prompt engineering techniques Creation of multimodal AI systems that can process manufacturing imagery, sensor data, and textual reports for predictive maintenance applications Design of automated decision-making systems for supply chain optimization using reinforcement learning and generative AI models

Success Factors

Mastery of the rapidly evolving LLM landscape requires continuous learning and adaptation to new architectures, fine-tuning techniques, and deployment strategies. Success depends on staying current with research developments from organizations like OpenAI, Anthropic, and academic institutions while translating these advances into practical manufacturing applications. Strong cross-functional collaboration skills are essential given the role's requirement to work with diverse stakeholders across US and European teams. The ability to communicate complex AI concepts to non-technical audiences in manufacturing and supply chain domains will determine project success and stakeholder buy-in. Production-focused mindset combining research innovation with operational reliability is crucial, as the role involves both experimental AI development and maintaining enterprise-grade ML pipelines. Balancing cutting-edge exploration with system stability and performance requirements is a key differentiator. Domain expertise development in manufacturing and supply chain processes will accelerate impact, as the most successful data scientists in this space understand both the technical AI capabilities and the specific business problems they're solving in industrial contexts.

Market Demand

Exceptionally high demand driven by the intersection of manufacturing digitization and generative AI adoption. The specific combination of LLM fine-tuning, RAG systems, and AI agents expertise represents a rare skill set with limited supply in the European market.

Important Skills

Critical Skills

Python proficiency with TensorFlow, PyTorch, LangChain, and Hugging Face Transformers forms the technical foundation for all daily responsibilities and represents the minimum viable skill set for contributing to the team's AI development initiatives. These tools are essential for model development, fine-tuning, and deployment in production environments. NLP and LLM fine-tuning experience is absolutely critical as it directly aligns with the role's core focus on developing and optimizing large language models for manufacturing applications. This expertise determines the ability to adapt general-purpose models to domain-specific use cases and achieve meaningful performance improvements. Cloud platform familiarity (AWS, GCP, Azure) is essential for deploying and scaling AI/ML models in production environments, as modern enterprise AI solutions require robust cloud infrastructure for performance, reliability, and cost-effectiveness. Cross-functional collaboration and communication skills are critical success factors given the role's requirement to work with diverse stakeholders across international teams and translate complex technical concepts for non-technical audiences in manufacturing domains.

Beneficial Skills

Experience with RAG architectures and vector databases (FAISS, Pinecone) provides significant advantage in developing sophisticated information retrieval systems that can process complex manufacturing documentation and historical data for intelligent decision-making applications. MLOps and model monitoring expertise becomes increasingly valuable as organizations mature their AI capabilities and require robust systems for maintaining model performance, detecting drift, and ensuring reliable production operations. Reinforcement learning knowledge offers strategic advantage for developing autonomous decision-making systems in manufacturing optimization, supply chain management, and predictive maintenance applications where sequential decision-making is crucial. Manufacturing or supply chain domain knowledge accelerates impact and effectiveness by enabling better understanding of business context, user needs, and practical constraints that influence AI solution design and implementation strategies.

Unique Aspects

This role offers rare exposure to the intersection of traditional manufacturing domain expertise and cutting-edge generative AI technologies, providing unique career positioning in the growing industrial AI market segment.
The combination of RAG systems, LLM fine-tuning, and AI agents development within a manufacturing context creates distinctive technical challenges not commonly found in generic AI roles, enhancing specialized expertise value.
Working across US and European time zones in a virtual-first environment develops valuable skills in asynchronous collaboration and international project management, increasingly important in global technology organizations.
The focus on 'LLM jailbreak methods' and advanced prompt engineering techniques indicates exposure to AI safety and robustness considerations, emerging as critical competencies in enterprise AI deployment.

Career Growth

Career advancement to senior individual contributor roles typically occurs within 2-3 years with demonstrated impact on product metrics and technical leadership. Transition to management or specialized roles like AI product management may take 3-5 years depending on business acumen development and stakeholder relationship building.

Potential Next Roles

Senior Data Scientist or Principal Data Scientist roles focusing on AI strategy and advanced research leadership within 2-3 years AI/ML Engineering Manager positions overseeing data science teams and AI product development timelines Product Manager roles specializing in AI-powered manufacturing solutions, leveraging technical expertise for strategic product decisions Technical consultant or solutions architect roles with AI/ML vendors serving the manufacturing industry Research scientist positions with technology companies or academic institutions focusing on industrial AI applications

Company Overview

QAD, Inc.

QAD, Inc. operates as an established enterprise software provider serving global manufacturing companies with cloud-based adaptive solutions. The company has demonstrated longevity in the manufacturing software space and is currently undergoing transformation to enhance its SaaS capabilities and AI integration. QAD serves industries including automotive, life sciences, packaging, consumer products, food and beverage, high tech, and industrial manufacturing, providing a diverse and stable business foundation.

QAD occupies a specialized niche in manufacturing enterprise software, competing with larger players like SAP and Oracle while maintaining focus on adaptive manufacturing solutions. The company's emphasis on AI/ML integration and virtual-first operations suggests strategic positioning for growth in the digitized manufacturing ecosystem. Their commitment to diversity, equity, and inclusion initiatives indicates a progressive organizational culture aligned with modern talent expectations.
The Paris-based role represents QAD's European expansion strategy and commitment to accessing global AI talent pools. Working with both US and European engineering teams provides exposure to international business practices and cross-cultural collaboration, valuable for career development in global technology organizations.
The virtual-first approach with occasional in-person collaboration suggests a results-oriented culture that values flexibility and work-life balance. The emphasis on cross-functional teamwork, knowledge sharing, and continuous learning indicates an environment supportive of professional development and innovation.
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