As a Data Science Practitioner, you will be instrumental in developing and implementing advanced decision-making frameworks and models powered by AI and machine learning to achieve impactful business outcomes. This involves rigorously measuring and validating the value delivered by AI/ML solutions.

Your daily activities will include collaborating with stakeholders to understand business challenges and translating these into robust analytical models. A key part of the role is continuously refining and optimizing these models to maximize their performance and business impact. You'll be expected to work independently and become a subject matter expert, actively contributing to team discussions and problem-solving initiatives.

- Spearhead the design and delivery of AI/ML decision-making frameworks and models to drive business results. - Measure and clearly articulate the value derived from AI/ML-based solutions. - Independently function as a subject matter expert and actively participate in team discussions. - Provide innovative solutions to work-related challenges and contribute to team problem-solving. - Collaborate with cross-functional teams to gather requirements and translate them into effective technical solutions. - Continuously monitor and enhance the performance of deployed models for optimal accuracy and reliability.

This role requires a minimum of 3 years of experience in Data Science, with a preference for candidates with experience in Deep Learning and Large Language Models (LLMs). Proficiency in data science methodologies, including strong knowledge of machine learning algorithms and their practical applications, is essential.

Candidates should possess experience in data preprocessing, feature engineering, and model evaluation. The ability to work with large datasets, perform in-depth data analysis, and extract actionable insights is crucial. Familiarity with programming languages like Python or R, along with a solid understanding of statistical concepts for predictive modeling, is expected. Experience with cloud platforms like Azure and tools such as Databricks and Power BI is beneficial. A proactive and self-sufficient approach is highly valued.