Director of Data Science/Machine Learning, CookUnity

• 10+ years of experience in data science, with at least 5 years in leadership roles managing data scientists or ML engineers
• Proven track record building and deploying ML models in production, particularly in personalization, recommendation systems, or predictive modeling
• Deep expertise in experimentation and causal inference, including A/B testing, incrementality measurement, and statistical rigor
• Strong product sense and business acumen—ability to identify high-impact opportunities and translate them into data science initiatives
• Experience in consumer tech, e-commerce, or marketplace businesses where personalization and user engagement are critical
• Excellent communication skills—ability to explain complex technical concepts to non-technical stakeholders and influence product strategy
• Hands-on technical proficiency in Python, SQL, and modern ML frameworks (scikit-learn, PyTorch, TensorFlow)
• Experience with cloud-based data infrastructure (AWS, GCP, Snowflake) and ML Ops tools (MLflow, Airflow, Kubeflow)

Desirable

• We're looking for a Director, Data Science/ML who will drive CookUnity's next phase of product innovation through forward-looking data science capabilities
• This role goes beyond traditional analytics—you'll be responsible for building the ML and experimentation foundation that enables personalized, intelligent product experiences at scale
• You'll own the strategic vision for how data science shapes our product roadmap
• You'll build and lead a team focused on predictive modeling, personalization, experimentation frameworks, and emerging ML capabilities that directly impact customer engagement, retention, and lifetime value
• This is a high-impact role for someone who thinks strategically about the future of product science while remaining hands-on in driving technical execution
• Define and execute the product data science strategy, identifying opportunities where ML and predictive analytics can unlock step-change improvements in customer experience and business outcomes
• Partner closely with Product, Growth, Engineering, and UX leadership to influence product roadmap with data-driven insights and forward-looking ML capabilities
• Act as a thought leader on emerging data science techniques (personalization, recommendation systems, causal inference, generative AI) and their application to product problems
• Translate complex product challenges into clear data science problems with measurable success criteria
• Own the end-to-end ML lifecycle for product use cases: problem framing, feature development, model training, deployment, monitoring, and iteration
• Partner with the Growth Data Science & Analytics team to align experimentation, measurement, and modeling efforts into a cohesive end-to-end data science ecosystem
• Build, mentor, and scale a high-performing product data science team capable of delivering both strategic insights and production ML systems
• Foster a culture of innovation, experimentation, and continuous learning within the data science organization
• Create career development pathways that attract and retain top data science talent
• Collaborate with Analytics Engineering to ensure seamless model deployment and monitoring
• Own and evolve personalization and recommendation systems that drive engagement and conversion across the customer journey
• Design and implement robust experimentation frameworks that enable rapid, high-quality product testing and learning
• Develop causal inference methodologies to understand true incrementality of product changes
• Ensure models are observable, explainable where needed, and continuously improved post-launch
• Define product success metrics and measurement frameworks that align with business objectives
• Build scalable dashboards and monitoring systems that provide real-time visibility into product performance
• Conduct deep-dive analyses on user behavior patterns to uncover opportunities for product optimization
• What Success Looks Like
• 6 months: Established product data science roadmap aligned with business priorities; shipped at least one high-impact ML model to production; built strong partnerships with Product and Engineering leadership
• 12 months: Scaled the product data science function with key hires; delivered measurable improvements in personalization and customer engagement metrics; implemented robust experimentation frameworks used across product teams