Associate Data Scientist - AI & Data Analytics
Listed on 2026-11-02
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IT/Tech
AI Engineer (Applied/Software), Data Scientist, Machine Learning/ ML Engineer, AI Evaluation
Location: New York
As an Associate Data Scientist, you will help solve business problems using statistics, machine learning, and data science techniques while contributing to generative AI and agentic AI workflows, along with open-source tools such as Python, R, and Tensor Flow, IBM tools, and AI application suites. You will prepare, analyze, and understand data to deliver insight, predict emerging trends, evaluate model and workflow performance, and provide recommendations to stakeholders.
Collaborating closely with diverse teams, you will help select appropriate modeling approaches, identify the critical data needed for analysis, and translate complex model results into clear recommendations for technical and non-technical audiences.
In your role, you may be responsible for:
- Prepare, cleanse, join, and analyze structured, semi-structured, and unstructured data for analytics, machine learning, GenAI, and agentic AI workflows.
- Implement and validate predictive, prescriptive, statistical, and machine learning models with a focus on measurable client and business outcomes.
- Design experiments, features, prompts, evaluation datasets, metrics, and tests to assess model outputs, workflow accuracy, reliability, bias, and business value.
- Build GenAI, RAG, and agentic AI workflows by evaluating retrieval quality, grounding, generated responses, tool-use outcomes, and end-to-end agent performance.
- Work in an Agile, collaborative environment with data scientists, data engineers, AI engineers, consultants, architects, and database administrators to bring analytical rigor and responsible AI practices to client challenges.
- Communicate with internal and external clients to understand business needs, select appropriate modeling techniques, and explain assumptions, limitations, and recommendations.
- Evaluate modeling results and present insights clearly to technical and non-technical audiences, translating findings into practical actions.
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