Data Scientist

Role Overview

• Work closely with stakeholders and software engineers to identify and design high-impact AI solutions, including RAG/Agentic-based applications, ETL pipelines that leverage LLMs to extract key insights/enhance data
• Leverage traditional machine learning, NLP techniques, and Large Language Models (LLMs) to analyze notes, transcripts and other unstructured text data
• Develop models for tasks such as topic modeling, classification, sentiment analysis, entity extraction, and error detection
• Establish robust evaluation frameworks (precision, recall, F1) and conduct iterative error analysis to continuously improve performance and reliability
• Design and build LLM-powered applications and services to assist technicians
• Apply best practices in prompt engineering (Chain-of-Thought, few-shot prompting, structured outputs), Retrieval-Augmented Generation (RAG), and agentic systems (tool usage, multi-step reasoning, API chaining, stateful workflows)
• Implement guardrails, validation layers, and hallucination mitigation strategies
• Architect, develop, and deploy scalable AI-powered APIs, applications and automated workflows
• Make deliberate design tradeoffs balancing latency, cost, performance, reliability, and lifecycle ownership
• Build modular, maintainable systems that integrate seamlessly with enterprise data sources and operational platforms
• Implement CI/CD pipelines for deployments
• Manage infrastructure using Infrastructure as Code (IaC), containerize services, and deploy to Kubernetes
• Establish monitoring, logging, versioning, and rollback strategies to ensure reliability, observability, and scalability in production
• Partner with engineering, operations, and business stakeholders to translate real-world challenges into AI solutions
• Clearly document architectural decisions and learnings
• Mentor team members and contribute to a culture of technical excellence

Requirements

• Master’s degree in Computer Science, Machine Learning, Data Science, Statistics, or a related quantitative discipline — or a PhD in a relevant field
• 3+ years of experience applying machine learning and AI in production environments, delivering measurable business impact
• Strong foundation in traditional ML and NLP (classification, regression, clustering, topic modeling, sentiment analysis) with experience designing robust evaluation frameworks (precision/recall/F1, experimentation, error analysis)
• Practical experience working with LLMs, including prompt engineering, structured outputs, and building Retrieval-Augmented Generation (RAG) systems
• Experience designing and implementing agentic AI systems, including multi-step reasoning workflows, tool/API orchestration, memory/state management, and production guardrails
• Experience building AI-powered applications and APIs, with an understanding of latency, scalability, cost optimization, and reliability tradeoffs
• Proficiency in Python and modern ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn), and experience developing and consuming REST APIs
• Experience building data pipelines (ETL/ELT), preprocessing structured and unstructured data, and strong SQL skills working with large-scale datasets
• Experience deploying models using Docker and Kubernetes, with familiarity in CI/CD, cloud platforms (GCP) and production monitoring
• Strong ability to translate ambiguous business problems into scalable AI systems and communicate effectively with both technical and non-technical stakeholders.

Tech Stack

• Cloud
• Docker
• ETL
• Google Cloud Platform
• Kubernetes
• Python
• PyTorch
• Scikit-Learn
• SQL
• Tensorflow

Benefits

• Comprehensive total rewards package highlighting competitive salary and bonus structures
• Minimum 3 weeks of vacation
• Flexible benefits plan to meet the needs of you and your family
• Generous company matched pension and share purchase programs
• Opportunity to give back to communities in which we work, live and serve
• Career growth and learning & development opportunities to develop your skills