Data & AI Scientist
Role details
Job location
Tech stack
Job description
We are looking for an experienced and agile Data Scientist - AI Engineer to join our dynamic team. You will design, develop, and deploy end-to-end AI systems, transforming complex data and business challenges into scalable, production-grade intelligence. Your work will combine the rigor of data science with the practicality of AI engineering, ensuring that advanced models and generative/agentic AI capabilities deliver measurable value across domains.
Your expertise in machine learning, time-series analysis, and generative AI will support multidisciplinary initiatives, particularly in predictive maintenance, operational optimization, and commercial intelligence. You will collaborate with cross-functional teams to turn ideas into impactful AI solutions applying robust engineering practices, responsible AI principles and modern MLOps to ensure scalability, security and compliance.
Your Role
- Translate complex business challenges into AI/ML solutions that deliver measurable value.
- Design, train, and deploy end-to-end AI systems across the lifecycle (data exploration, modeling, validation, deployment, and MLOps).
- Develop predictive maintenance models using sensor and equipment data to optimize uptime and performance.
- Apply machine learning to commercial use cases such as churn prediction, customer segmentation, and recommendation systems.
- Experiment with generative and agentic AI frameworks to accelerate innovation.
- Collaborate cross-functionally with product, engineering, and business teams to ensure alignment and impact.
- Implement scalable and automated pipelines using cloud platforms (Azure ML, AWS SageMaker, Databricks).
Requirements
- Master's or PhD in Computer Science, Data Science, or related fields.
- 5+ years (Master's) or 3+ years (PhD) hands-on experience in AI/ML development and deployment.
- Strong proficiency in Python, SQL, and Spark; with libraries such as Scikit-learn, TensorFlow, and PyTorch.
- Experience with MLOps tools and frameworks (MLflow, CI/CD, model monitoring).
- Proficiency in cloud-native ML platforms (Azure ML, AWS SageMaker, Databricks).
- Excellent communication skills for engaging both technical and business stakeholders.
- Experience in predictive maintenance, commercial AI, or similar applied AI domains., * Experience with large language models, generative AI, or agentic AI frameworks.
- Familiarity with data governance, ethical AI practices, and model interpretability.
- Experience leading AI projects from concept to production deployment.
Join us to create impactful AI solutions that drive smarter decisions, improve operational efficiency, and advance the next generation of intelligent systems.