Lead Data Scientist

🔥 12 hours ago

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DemandTec

WebsiteLinkedInAll Job Openings

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.

📋 Description

• Own the technical roadmap for ML/AI models powering price optimization, demand forecasting, promotion effectiveness, and markdown recommendations. • Architect and lead development of GenAI-powered agents and copilots (e.g., pricing copilots, demand intelligence agents) in partnership with engineering and product. • Set technical standards for model development, validation, and MLOps across the data science organization. • Mentor, coach, and grow a distributed team of data scientists, including direct oversight of the China and Poland-based team. • Partner with product and engineering leadership to translate retail and trade-promotion business problems into Data Science solutions. • Make build-vs-buy calls on LLM/GenAI tooling and vendor platforms together with ENG. • Present model performance, technical roadmap, and AI strategy to executive leadership and, where relevant, customers and prospects. • Track emerging AI/ML techniques and assess their applicability to retail and CPG use cases. • Develop scalable feature engineering workflows over large retail datasets.

🎯 Requirements

• 7+ years of experience in data science or applied ML, including 2+ years leading data scientists or a technical team. • Proven track record shipping production ML models at scale. • Experience designing, building, and shipping models for price elasticity, demand forecasting, promotion effectiveness, and similar retail/CPG use cases. • Strong communication skills — able to translate technical work into business impact for executives and customers. • 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.