Job Type: Direct hire, Full-Time
Compensation: $170,000 – $185,000 base + 10% bonus
Location: Remote (must be based in the U.S.)
Our edtech client is looking for a director of data science. As director, you will lead the data science strategy and execution behind one of their newest platforms. This role is about building the intelligence layer that orchestrates real-time decisions across the full arc of the student journey, from first inquiry through completion, at a scale no single institution could achieve alone.
Responsibilities
- Design and build advanced machine learning models that power predictive and conversational AI experiences across student recruitment, enrollment, and persistence.
- Own the end-to-end lifecycle of production ML models: discovery, experimentation, deployment, governance, monitoring, and continuous improvement.
- Develop and maintain next-best-action, propensity, and intervention models to guide outreach, support, and escalation decisions in real time.
- Implement robust MLOps practices, including model versioning, performance tracking, and automated retraining strategies.
- Architect systems to extract high-signal insights from voice, text, and behavioral interaction data using classical NLP, large language models, and generative AI.
- Integrate signals across channels (calls, chats, SMS, email, in-app behavior) to build a unified, real-time view of student intent, friction points, and sentiment.
- Partner with engineering to productionize LLM- and generative-AI-based features that enhance student and advisor experiences while maintaining safety and compliance.
- Define approaches to data labeling, redaction, and anonymization to ensure privacy and security of conversational data.
- Lead the experimentation roadmap (A/B tests, feature rollouts, behavioral studies) to ensure product decisions are grounded in rigorous statistical evidence.
- Define standards for evaluating generative and predictive features, including offline metrics, online KPIs, and human-in-the-loop review processes.
- Build analytical frameworks that connect product changes to specific movements in student outcomes and operational efficiency.
- Collaborate with Product, Design, and Engineering to instrument features, define success metrics, and interpret experiment results.
- Lead and grow a high-performing data science team, setting clear expectations around quality, ownership, and delivery.
- Coach team members across levels, supporting both technical depth and stakeholder influence.
- Partner closely with senior leaders in Product, Engineering, Operations, and University Partnerships to align data science initiatives with strategic priorities.
- Translate complex modeling approaches into clear narratives and options that drive executive decisions and resource allocation.
Requirements
- 10+ years in data science, machine learning, or advanced analytics roles.
- 5+ years leading multidisciplinary data science or analytics teams in a product or platform context.
- Hands-on experience building or evaluating predictive AI systems in voice or text-based products (e.g., chatbots, contact centers, virtual assistants).
- Proven production machine learning experience: model development, deployment, governance, monitoring, and lifecycle management.
- Strong command of Python, SQL, and modern ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn).
- Experience designing evaluation frameworks for LLM or generative AI features, including human evaluation methodologies.
- Demonstrated expertise in designing and analyzing experiments (A/B testing, phased rollouts, behavioral experiments).
- Master’s or Ph.D. in Computer Science, Data Science, Statistics, or a related quantitative field — or equivalent practical experience.
Preferred Qualifications
- Familiarity with generative AI applications in product, customer experience, or agent-assist workflows, including agentic workflow design.
- Experience with next-best-action, recommendation, or real-time intervention modeling in high-stakes domains.
- Hands-on work with cloud data platforms and distributed data technologies (e.g., Spark, Databricks, Snowflake, BigQuery).
- Background in EdTech, higher education, or other mission-driven industries where outcomes and access are central.
- Experience partnering with go-to-market, operations, or student success/customer success teams, not just product and engineering.
