Director, Data Science

Overview

The Data Science Director is accountable for establishing and scaling deep analytics and predictive intelligence capabilities across the enterprise to increase both the operational effectiveness and efficiencies of our internal functions. In this role, you will define where machine learning creates value, build the team and infrastructure to deliver it, and embed data science models & tools into the strategic organization-wide decision-making. This is a foundational role for someone who understands how to translate technical capabilities into business impact across end-to-end customer lifecycle.

Basic Qualifications

• Experience in data science, analytics, machine learning, GenAI or related quantitative fields, ideally having managed a high-performing teams.
• Experience deploying production ML systems that delivered measurable business outcomes (revenue growth, cost reduction, efficiency gains).
• Experience operating in enterprise software, SaaS, ERP, or B2B technology environments.
• Experience applying GenAI and/or Agentic AI to real-world enterprise use cases.
• Deep expertise in machine learning, statistical modeling, optimization, and causal inference, with hands-on experience with modern ML tech stacks (Python, Spark, TensorFlow/PyTorch, cloud platforms), as well as understanding of MLOps practices, model lifecycle management, and production ML infrastructure.
• Experience designing and deploying GenAI solutions, including LLM fine-tuning, prompt engineering, and retrieval-augmented generation (RAG), having familiarity with GenAI/LLM applications in enterprise contexts, including Agentic AI frameworks and orchestration tools (e.g., LangChain, AutoGen, Semantic Kernel).
• Experience influencing C-suite and drive organizational change, as well as having had experience managing P&L impact and presenting ROI of analytics investments to senior leadership.
• Full-Stack Data Ecosystem Experience, having fluency across the data stack, includes modern platforms (e.g., Snowflake), orchestration tools (e.g., dbt), and cloud-native architectures, plus strong ML/AI proficiency for scalable solutions.
• Value Creation Mindset, prioritize innovation and business value creation when shaping data and analytics strategies, while building in rigor and scalability in parallel.
• Experience in Holistic Analytics Design, having the ability to assess a business problem, identify the insights required, determine the necessary data, and design end-to-end analytics solutions.

Preferred Qualifications

• Experience with AWS AI tooling and services, e.g., Bedrock, SageMaker.
• Background in customer analytics (Demand Gen funnel analytics, product adoption analytics)