Senior Applied Data Scientist
Posted Updated
- As a Senior Data Scientist, you will drive the development of high-impact, scalable AI solutions, moving beyond prototypes to production systems
- We are looking for an experienced data scientist with the proven ability to manage project timelines and deliver high-quality code that aligns with business goals
- You will combine strong problem-solving skills with a collaborative approach to ensure team success
- This role combines hands-on technical leadership with strategic oversight in a high-impact environment focused on innovation and continuous development
- Develop and deploy production-level AI models and data-driven solutions that align with strategic business objectives
- Tune and optimize AI models to improve their accuracy, efficiency and overall fitness for purpose in various applications
- Evaluate third-party AI solutions on both functional and non-functional aspects, assessing their suitability based on performance, scalability, and business needs
- Establish and apply coding standards to maintain robust and clean code
- Work closely with Software Engineers and multi-disciplinary teams to define project requirements and direction, and establish development roadmaps
- Proficiency in Object-Oriented programming in Python with expertise in key data science and machine learning libraries (e.g. TensorFlow, PyTorch and Scikit-Learn)
- Proven ability to establish and uphold coding standards to ensure high-quality, maintainable code
- Excellent communication skills for summarizing complex technical concepts clearly and effectively to both technical and non-technical partners
- Extensive experience applying data science methods to solve real-world problems with a proven track record of deploying AI models and solutions to production
- Strong statistical analysis skills, with a foundation in probability, to support robust model development and interpretation
- Expertise in cloud technologies (emphasis on Azure and/or AWS) and deployment practices, ensuring seamless and scalable delivery
- Working knowledge of DevOps practices, including code versioning, CI/CD pipelines, automated testing, and monitoring, to streamline development workflows and improve deployment reliability