Engineer - Data Science, with Consulting domain having 0 - 2 years of experience requires a strong foundation and budding expertise in key areas pertinent to AI and Data Science.
Proficiency in rag is essential for effective representation and management of data, ensuring that insights derived are both relevant and actionable.
Candidates should demonstrate foundational understanding and application of skills in data preprocessing or feature selection in machine learning models.
Langgraph expertise is crucial as it involves the graphical representation of language data, facilitating better understanding and communication of complex data relationships.
A solid grasp of AI principles and applications is mandatory, with candidates expected to have foundational knowledge in designing and deploying AI solutions.
Python is a core programming language in this domain, and candidates should exhibit foundational proficiency, including writing clean, efficient code for data analysis.
Candidates are required to hold a Master's in Data Science or a Bachelor's in Computer Science with a specialization in Artificial Intelligence or Machine Learning.
Possessing certifications such as the IBM Data Science Professional Certificate and the Microsoft Certified: Azure Data Scientist Associate is preferred as they validate proficiency in industry-standard practices.
Collaborate with cross-functional teams to gather and understand data requirements from stakeholders, ensuring alignment on project goals.
Apply data science techniques using Python to analyze and interpret complex datasets, contributing to data-driven decision making.
Utilize graph analysis tools like rag and langgraph to uncover insights and patterns within data.
Assist in the development of AI models and algorithms to enhance predictive capabilities and improve operational efficiency.
Conduct research to stay up-to-date with the latest trends and advancements in data science and AI technologies.
Participate in the documentation of processes, methodologies, and findings to support knowledge sharing within the team.
Communicate findings and insights effectively to both technical and non-technical stakeholders, fostering a collaborative environment.
Embrace continuous learning through hands-on projects and mentorship opportunities to develop technical skills and professional growth.
Job Classification
Industry: Advertising & MarketingFunctional Area / Department: Data Science & AnalyticsRole Category: Data Science & Analytics - OtherRole: Data Science & Analytics - OtherEmployement Type: Full time