Data Scientist Senior Consultant II

**Data Scientist Senior Consultant II**

**Job Description**

In this role, you will design and build AI-driven Insight Studio for the Scholar product, transforming large-scale customer conversations into actionable, real-time insights. You will hands-on build and productionize AI systems, integrating LLMs, RAG pipelines, and AI agents into scalable, reliable applications that directly power business decisions.

**Key Responsibilities**

– Translate complex business problems into end-to-end AI solutions, leveraging machine learning and Generative AI techniques aligned with product and business goals.
– Design, build, and operate production-grade AI systems for conversational understanding, summarization, classification, and insight generation from large-scale customer interaction data.
– Architect and implement Retrieval-Augmented Generation (RAG) pipelines, integrating structured and unstructured data sources to deliver context-aware, grounded, and explainable AI outputs.
– Build and optimize vector search and embeddings-based systems, enabling semantic search, similarity matching, and efficient information retrieval at scale.
– Design and implement AI agent workflows, orchestrating multi-step reasoning, tool usage, and decision-making using frameworks such as LangChain, LangGraph, or similar.
– Apply prompt engineering and LLM orchestration techniques to improve output quality, consistency, latency, and cost efficiency in production environments.
– Integrate foundation models via platforms such as AWS Bedrock, including fine-tuning, evaluation, and deployment strategies tailored to business use cases.
– Build and maintain scalable data pipelines and distributed processing systems to support real-time and batch AI workloads.
– Own end-to-end lifecycle of AI/ML solutions, including data preparation, model development, evaluation (including LLM evaluation and guardrails), deployment, and continuous improvement.
– Implement MLOps and LLMOps practices, including CI/CD pipelines, versioning, monitoring, and automated evaluation for both traditional ML and AI systems.
– Monitor and optimize AI systems using observability tools (e.g., Datadog, CloudWatch), ensuring performance, reliability, and cost control in production.
– Analyze model outputs, communicate findings, and iterate on solutions to continuously improve business impact and user experience.

**Essential Skills**

**Data Science & AI Expertise**

– Strong proficiency in Python for AI/ML 
– Proven experience building and scaling production-grade AI/ML systems
– Deep understanding of data pipelines, distributed systems, and scalable architectures
– Strong grounding in MLOps / LLMOps workflows and lifecycle management
– Practical experience building applications using:
– **Large Language Models (LLMs)** (prompting, fine-tuning, orchestration)
– **Retrieval-Augmented Generation (RAG)** architectures
– **Embeddings and vector search systems**
– **AI agents and multi-step reasoning workflows**
– Strong understanding of **LLM evaluation, guardrails, and responsible AI practices**

**Frameworks & Tooling**

– Hands-on experience with:
– **LangChain, LangGraph, LlamaIndex** or similar orchestration frameworks
– Experience designing **modular, reusable AI pipelines and components**

**Data & Storage**

– Experience with **vector databases** and modern data storage systems
– Familiarity with handling **large-scale structured and unstructured datasets**

**Cloud & AI Platforms**

– Experience with:
– **Amazon SageMaker or similar ML platforms**
– **AWS Bedrock** for foundation model access and deployment
– AWS services such as **Lambda, S3, ElastiCache**
– Experience deploying **LLM-powered applications in cloud-native architectures**

**Observability & Reliability**

– Experience with:
– **Datadog, AWS CloudWatch**
– Strong ability to **monitor, debug, and optimize AI systems in production**

**Additional Platforms**

– Experience with **Microsoft Fabric** or similar analytics platforms

**Primary Skills**

Artificial Intelligence Markup Language, Business Case Analyses, Data Analytics, Predictive Modeling, Waterfall Model