Director Date Science

Department: Sales

Employment Type: Full Time

Location: US-Remote

Reporting To: Shaleen Jain

Description

A Typical Day in the Life Includes:

• Build and lead a data science team (approximately 6) from the ground up, setting vision, operating model, and team structure; with a focus on data science models & tool.
• Recruit, develop, and retain top-tier talent across data science, engineering, and related technical roles.
• Create a culture of experimentation, analytical rigor, and strong business partnership across RevOps, Marketing, Sales, Customer Success, Delivery, IT, and more.
• Identify and prioritize high-ROI use cases spanning demand generation, conversion optimization, customer adoption, retention, pricing, forecasting, and operational efficiency.
• Translate business opportunities into actionable data science projects (e.g., win/loss analytics, churn root-cause analysis, AE/BDR performance insights).
• Deploy enterprise-scale ML systems with robust monitoring, governance, and MLOps infrastructure for rapid, reliable model deployment and decision dashboards.
• Architect scalable ML/AI infrastructure for batch and real-time inference, adopt emerging technologies (LLMs, AutoML, Agentic AI), and set standards for model lifecycle management.
• Influence C-suite decisions with predictive insights, drive organizational data literacy, and represent the company externally through thought leadership.

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, churn prediction, lifetime value, segmentation, etc.).
• Publications, patents, or speaking engagements demonstrating thought leadership.