Work on data science projects in close collaboration with Data Engineering, Application development, Product owners and business leaders to deliver high-value business capabilities
Build and enhance AI-powered chatbot capabilities to provide intelligent, context-aware client support
Develop intelligent tools and systems that power conversational AI, including search, recommendation, and content retrieval capabilities
Build user understanding and personalization models to identify client cohorts and tailor interactions
Implement and optimize Model Context Protocol (MCP) servers to enable seamless integration between AI agents and enterprise systems
Leverage internal and external data to understand client's company-level priorities and deliver targeted support
Maintain high-quality data science solutions with proper documentation and code-reusability principles
Stay on top of fast-moving AI/ML models and technologies, particularly in the LLM and conversational AI space
Collaborate with engineering and product teams to launch MVPs and iterate quickly
Independently plan and drive data science projects that deliver clear business value

Requirements

1-3 years hands-on experience building chatbots, conversational AI systems, or other machine learning/artificial intelligence applications to drive business impact
Bachelor's or Master's Degree in a quantitative field (math, computer science, engineering, etc.) required
Strong communication skills in technical and business domains with demonstrated ability to translate quantitative analysis into actionable business strategies
Working experience in some of the following data science areas: Large Language Models (LLMs) and Generative AI, Natural Language Processing and text mining, Conversational AI and chatbot development, Search and Recommendation systems, Prompt engineering and LLM fine-tuning, AI agent architectures and orchestration
Familiarity with Model Context Protocol (MCP) and building tools for AI agents
Strong working knowledge of Lean product principles, software development lifecycle, and machine learning life cycle
Practical, intuitive problem solver with ability to translate business objectives into actionable data science tasks and implement latest ML research
Experience and proficiency with Python, machine learning tools (e.g., scikit-learn, spacy, nltk), deep learning frameworks (e.g., huggingface, pytorch, tensorflow), LLM frameworks (e.g., LangChain, LlamaIndex), SQL/relational databases (e.g., Oracle), NoSQL databases (e.g., MongoDB, graph database), vector databases, distributed machine learning (spark), Linux and shell scripting
Experience with cloud computing services such as AWS or Azure ML
Ability to work collaboratively across product, data science and technical stakeholders in a culture that thrives on feedback
Bias for action and client outcome oriented.

Benefits

Competitive salary
Generous paid time off policy
Charity match program
Group Medical Insurance
Parental Leave
Employee Assistance Program (EAP) and more!

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

chatbot developmentconversational AImachine learningnatural language processinglarge language modelsgenerative AIprompt engineeringdata sciencePythonSQL

Soft Skills

strong communicationproblem solvingcollaborationaction-orientedclient outcome focus

Certifications

Bachelor's DegreeMaster's Degree