About CookUnity:
Food has lost its soul to modern convenience. And with it, it has lost the power to nourish, inspire, and connect us. So in 2018, CookUnity was founded as the first-of-its-kind platform that connects the world with the source of truly great food: chefs. Today, CookUnity delivers 50 million meals a year from the industry’s best chefs to homes all over the country. Fresh. Ready-to-eat. And crafted with the passion that nourishes body and soul.
Unwilling to stop there, CookUnity is expanding beyond delivery to become an ever-innovating marketplace focused on our singular mission: empower Chefs to nourish the world.
If that mission has you hungry in more ways than one, you’ve found the right job posting.
The Role:
We're looking for a Senior Manager, 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.
As Senior Manager, 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.
Responsibilities:
Strategic Vision & Product Partnership
- 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.
Team Leadership & Development
- 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
Advanced Analytics & ML Capabilities
- 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
Product Measurement & Impact
- 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
Qualifications:
- 8+ years of experience in data science, with at least 3 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)
Preferred requirements:
- PhD or Master's degree in Computer Science, Statistics, Mathematics, or related quantitative field
- Experience with real-time ML systems and feature stores
- Background in recommendation systems or two-tower/multi-modal embeddings
- Familiarity with generative AI and LLM applications in product contexts
- Experience building data science teams from scratch or through periods of rapid growth
- Prior work in subscription businesses or retention-focused products
- Knowledge of modern product analytics tools (Amplitude, MixPanel, Looker)
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