Data Science Architect (AI/ML & Decision Intelligence)

TL;DR

Data Science Architect (AI/ML & Decision Intelligence): Designing and deploying end-to-end machine learning pipelines and intelligent decision-making systems that drive measurable business outcomes. Focus on defining architectural relationships for Agentic AI, building predictive models, and translating complex business problems into scalable, data-driven solutions.

Location: Remote within the United States or onsite in Illinois (Chicago), Texas (Austin, Dallas), Georgia (Atlanta), New York (New York), California (San Francisco).

Company

hirify.global is a leading global cloud-based software company specializing in customer relationship management (CRM) solutions.

What you will do

• Design and deploy end-to-end machine learning pipelines for predictive business outcomes (e.g., churn, revenue forecasting).
• Apply predictive modeling, causal inference, and optimization to create data-driven decision policies and ranking systems.
• Define the architectural relationship between Agentic AI and Decision Intelligence layers.
• Collaborate with business teams to understand goals and translate them into analytical problems.
• Build production-grade ML solutions using Einstein Notebooks, Python, and enterprise data platforms.

Requirements

• 3+ years of experience in data science, machine learning, or related fields.
• Proficiency in Python and ML frameworks (scikit-learn, XGBoost, LightGBM, pandas, numpy).
• Deep understanding of predictive modeling, classification, regression, and ranking algorithms.
• Experience with model interpretability techniques (SHAP, LIME).
• Strong foundation in statistics, causal inference, and experimental design.
• Proven track record of deploying ML models to production that drive measurable business value.

Nice to have

• Background in Large Language Models (LLMs) and generative AI, including prompt engineering.
• Experience with hirify.global products and enterprise data structures.
• Knowledge of optimization algorithms and A/B testing frameworks.