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Mid-level generalist data science role, hybrid, broad ML/LLM requirements, and known brand increase applicant competition.
Core data science skills are transferable across industries, though finance domain preference raises specificity slightly.
Explicit 2–6 years plus mandatory Python, SQL, ML and AWS exposure leads to medium strictness in shortlisting.
Translate complex business problems into data science, machine learning, or AI use cases and deliver end-to-end solutions.
Develop, deploy, and validate machine learning, statistical, and generative AI models using Python, SQL, and AWS cloud services.
Present findings and collaborate with cross-functional teams to implement scalable AI-driven insights and solutions.
2-6 years experience in Data Science, Advanced Analytics, Machine Learning, or AI-related roles.
Proficiency in Python and SQL for data analysis, modeling, and solution development.
Graduate or Post-graduate degree in Data Science, Statistics, Mathematics, Computer Science, Economics, Engineering, Finance, Business Analytics, or a related quantitative discipline.
Exposure to AWS cloud platforms and familiarity with deploying analytical, ML, or AI solutions using AWS (e.g., Lambda, S3, Bedrock).
Experienced in handling data science projects end-to-end including business understanding, modeling, validation, and stakeholder communication.
Strong working knowledge of modern generative AI concepts such as LLMs, prompt engineering, Retrieval-Augmented Generation, and foundation model platforms like Amazon Bedrock.
Comfortable working independently to manage deliverables and collaborate effectively across business, technology, engineering, and analytics teams.