Data Scientist

Education
Bachelor's degree in AI, Data Science, or Computer Science (required)
Internships accepted as job experience
Acceptable equivalent technical degrees: Applied Statistics, Data Science, Business Analytics, Business Intelligence & Analytics, Computer Science, Computer Science + X, Engineering (Aerospace, Electrical, Mechanical, Computer, Industrial, Agricultural, etc.), Informatics, Information
Systems Management, Mathematics, MBA with Technical Undergrad, Predictive Analytics, Statistics
Master's or PhD also acceptable




Experience
5 7 years of professional experience
2+ years of experience applying Python (NumPy, SciPy, pandas, etc.) to solve business challenges
Experience with cloud technologies (AWS, Azure, Google Cloud, etc.)




Top 3 Critical Skills
Built agents in Dataiku & Snowflake Cortex (structured data) - critical
Workflow automation in Dataiku recipes & Python (writing programming logic)
Experience with LangGraph, Langchain, CrewAI




Technical Requirements
Multi-agent architecture and orchestration patterns (agent collaboration, planning, delegation, workflow orchestration)
Reusable agent frameworks and reference architectures
Security, access control, and governance (RBAC, personas, authorization models, guardrails)
Auditability and compliance considerations
Enterprise integrations (Microsoft Teams, SharePoint, email, PDFs, enterprise data sources)
Event-driven and batch processing architectures
API and workflow integration best practices
Agent evaluation, testing, and observability (offline/online evaluation, groundedness, relevance, hallucination detection, task success measurement)
Automated regression testing, monitoring, observability, and performance tracking
Prompt engineering and agent design patterns (prompt optimization, reusable prompts, skills, tools, agent design frameworks)
Human-in-the-loop workflows (approval processes, escalation paths, feedback loops, continuous improvement)
Dataiku application development and UX (Dataiku WebApps, production-ready front-end development, enterprise adoption best practices)
Deployment, operationalization, and scalability (deployment automation, monitoring, governance)
Cost and performance optimization (LLM selection, latency, token consumption, operational cost management, performance tuning)
MCP experience in multi-agent systems for Agent-to-Agent communication (desired)
Advanced data analysis and statistical methods (regression, hypothesis testing, ANOVA, statistical process control) (desired)
Machine learning techniques (Clustering, Logistic Regression, CART, Random Forests, SVM, Neural Networks) (desired)
Design/development of heavy equipment (automotive, aerospace, engine, transmission, construction, mining, industrial) (desired)




Soft Skills
Team player (required)
Good communication skills (desired)
Strong initiative
Interpersonal skills
Ability to communicate effectively
Planning and organization skills
Teamwork
Decision-making skills
Strong concern for customers
Strong focus on continual learning in the Analytics field
Strong initiative to research and apply new methods and digital technologies








Job Responsibilities - Data Scientist 3
Understand the objective (of the assigned project/use case)
Write code in Snowflake & Dataiku to build AI Agents
Test output and update code
Build AI Agents in Dataiku & Snowflake Cortex using structured data
Use AI Agents to create alerts and automate workflows
Perform workflow automation in Dataiku recipes & Python (write programming logic)
Apply multi-agent architecture and orchestration patterns (agent collaboration, planning, delegation, workflow orchestration)
Develop reusable agent frameworks and reference architectures
Implement security, access control, and governance (RBAC, personas, authorization models, guardrails)
Ensure auditability and compliance considerations
Build enterprise integrations and data access patterns (Microsoft Teams, SharePoint, email, PDFs, enterprise data sources)
Work with event-driven and batch processing architectures
Apply API and workflow integration best practices
Conduct agent evaluation, testing, and observability (offline/online evaluation, groundedness, relevance, hallucination detection, task success measurement)
Perform automated regression testing, monitoring, observability, and performance tracking
Apply prompt engineering and agent design patterns (prompt optimization, reusable prompts, skills, tools, agent design frameworks)
Design human-in-the-loop workflows (approval processes, escalation paths, feedback loops, continuous improvement mechanisms)
Develop Dataiku applications and user experience (Dataiku WebApps, production-ready front-end development, enterprise adoption best practices)
Handle deployment, operationalization, and scalability (deployment automation, monitoring, governance)
Perform cost and performance optimization (LLM selection, latency, token consumption, operational cost management, performance tuning)
Upskill the current team in the latest Dataiku Agentic AI technology
Share knowledge and teach this new technology to team members
Establish best practices reusable across multiple AI programs and use cases
Apply theory and concept in contributing to solutions in the assigned area
Perform data collection and analysis
Conduct development, validation, application, and refinement of statistical models
Apply related digital technologies in processing both inputs and outputs from models
Participate in daily standup meetings
Collaborate on a project with 3 other people (including project leader)




Disqualifiers / Red Flags / Overqualifications
Remote work
No past or additional job titles/roles that would provide comparable background