AI Data Scientist

Key Responsibilities:  

• Develop and implement predictive models, machine learning algorithms, and AI solutions, and AI agents to solve business challenges.
• Clean, preprocess, and analyze large-scale datasets (structured and unstructured) to extract meaningful patterns.
• Implement reinforcement learning (RL), natural language processing (NLP), and multi-agent systems to solve complex industrial challenges.
• Translate business requirements into modular, reusable AI agent architectures.
• Build end-to-end AI agent pipelines, including data ingestion, model training, validation, and deployment in production environments.
• Conduct testing and performance monitoring of AI agents to ensure scalability and reliability.
• Collaborate closely with software engineers, product managers, and business stakeholders to integrate AI solutions into products and operations.
• Document methodologies, findings, and model performance comprehensively to ensure reproducibility, transparency, and knowledge sharing across the organization.

Qualifications:

• Strong statistical analysis and problem-solving skills.
• Ability to translate business requirements into technical solutions.
• Excellent communication and teamwork skills.
• Strong understanding of multi-agent systems, decision optimization, and autonomous learning.
• Ability to design scalable, fault-tolerant AI architectures.
• Innately curious, highly adaptable, and possessing strong intellectual curiosity with a continuous learning mindset.
• Proactive problem-solver with a knack for identifying opportunities to leverage data and AI for business value. 
• Proven ability to present complex analytical findings clearly and concisely to both technical and non-technical stakeholders, utilizing data visualization tools.
• Degree in Data Science, Computer Science, AI, or related field.
• 3-5+ years of experience in AI/ML model development and deployment.
• Proven expertise in RapidMiner and/or similar solutions for data preparation, modeling, and automation.
• Proficiency in programming languages (Python, R, SQL).
• Experience with big data tools (Snowflake, Spark, Hadoop).
• Familiarity with agent-based modeling tools (e.g., LangChain, AutoGen, RLlib).
• Practical experience with version control systems (e.g., Git) for collaborative code development and model management.
• Experience with cloud platforms (e.g., AWS, Azure, GCP) for data storage, processing, and model deployment.
• Demonstrated experience in deploying and managing machine learning models in production environments (MLOps). 
• Preferred Qualifications: Experience with Mendix for low-code development and deploying AI-driven applications, Certification in RapidMiner or AI agent frameworks (e.g., Microsoft Autogen, Google Dialogflow), Background in industrial AI use cases (predictive maintenance, supply chain automation, digital twins).