
51 - 200 employees
☁️ SaaS
🛒 Retail
🤖 Artificial Intelligence
SaaS • Retail • Artificial Intelligence
DemandTec is an AI-powered SaaS platform that helps retailers and suppliers optimize lifecycle pricing, promotions, markdowns, and trade fund collaboration. It unifies pricing, promotions, markdowns, and deal management on a single platform, applying demand science and automation to protect margins, improve consumer price perception, and streamline supplier-retailer collaboration and deal reconciliation.
🕒 July 6
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51 - 200 employees
☁️ SaaS
🛒 Retail
🤖 Artificial Intelligence
SaaS • Retail • Artificial Intelligence
DemandTec is an AI-powered SaaS platform that helps retailers and suppliers optimize lifecycle pricing, promotions, markdowns, and trade fund collaboration. It unifies pricing, promotions, markdowns, and deal management on a single platform, applying demand science and automation to protect margins, improve consumer price perception, and streamline supplier-retailer collaboration and deal reconciliation.
• Build and validate ML models supporting pricing, promotion, and markdown optimization. • Contribute to GenAI initiatives — Build vertical-domain agents and agent clusters. • Partner with Data Engineering to build robust, production-grade data pipelines. • Perform exploratory data analysis and translate retail/CPG data into actionable insights. • Build dashboards and visualizations to communicate findings to product and business stakeholders. • Participate in code review, model validation, and documentation practices. • Develop scalable feature engineering workflows over large retail datasets.
• 3+ years of experience in data science or applied ML roles. • Experience designing, building, and shipping models for price optimization, demand forecasting, promotion optimization, and similar retail/CPG use cases. • Strong analytical and problem-solving skills; comfortable working with imperfect, real-world retail data. • Proficiency in Python, SQL, and machine learning frameworks (e.g., Scikit-learn, TensorFlow, PyTorch). • Familiarity with GenAI frameworks (e.g., LLMs, Dify, LangChain, RAG pipelines). • Familiarity with cloud-based data platforms (e.g., AWS, GCP, Azure) and big data technologies (e.g., Spark, Hadoop, Databricks). • Experience with data visualization tools (e.g., Power BI, Tableau) and modern MLOps practices. • Hands-on experience with modern data tooling (e.g., dbt, Airflow, or similar orchestrators) and columnar/analytical engines.
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