Demonstrates proficiency in at least one of the five areas: Artificial Intelligence, Generative Ai, Natural Language Processing, Machine Learning, and Forecasting. Expected to deepen this expertise further.
Translates data-related client requirements into project tasks and deliverables, with an understanding of complex problems and solutions.
Drives data solutions within projects, with a focus on innovation and value creation.
Provides technical guidance and support to team members on data science methodologies, tools, and algorithms.
Acts as a point of escalation for data science-related issues within the project, working towards efficient resolution.
Reviews and ensures adherence to performance parameters for data science tasks, with a focus on data quality and model performance.
Balances data science workload within the team, considering the skills and capacities of team members.
Champions learning initiatives for upskilling and cross-skilling in data science within the team, including knowledge sharing sessions and promoting best practices.
Contributes to the development of new data science solution offerings, leveraging insights from project work.
Encourages team members to build technical expertise and innovate within the realm of data science.
Begins to build and nurture client relationships with a focus on identifying opportunities to add value through data science.
Starts to develop proficiency in at least one of data engineering, MLOps, product engineering, and cloud services, and applies this knowledge within projects.
Begins to contribute to the organization's thought leadership in data science, such as writing articles, giving presentations, or contributing to internal forums.
Actively identifies and communicates opportunities for the organization to enhance its data science capabilities, including new tools, methodologies, or service offerings.
Keeps an eye on industry trends in data science to ensure personal growth and the competitive advantage of the organization
