Build advanced recommendation models, such as Next Best Offer systems, leveraging cutting-edge techniques.
Create and own the end-to-end lifecycle of machine learning models, from data preprocessing and feature engineering to production deployment.
Design and deploy scalable machine learning architectures that integrate seamlessly into customer-facing applications.
Continuously evaluate and improve model accuracy, scalability, and computational efficiency.
Stay up-to-date with the latest advancements in machine learning to ensure best-in-class solutions are applied to business problems.
Report to the Director of Growth Data Science.
Work with the Growth Data Science team to drive measurable impact on user engagement and business outcomes.
Collaborate with cross-functional partners in Product, Engineering, Analytics, and Marketing to deliver data-driven solutions.
Identify new opportunities to uncover actionable insights from large-scale datasets.
Grow the effectiveness of the PLG funnel through optimized model performance and personalization.

5+ years of direct experience in data science or machine learning, with a specific focus on recommendation systems.
Proven experience deploying machine learning models end-to-end in a SaaS or PLG environment.
Strong proficiency in Python and deep learning frameworks such as TensorFlow or PyTorch.
Hands-on experience with GNN, Transformers, or other advanced ML architectures.
Solid understanding of MLOps best practices, data pipelines, and containerization (Docker, Kubernetes).
Expertise in working with large-scale datasets and distributed computing tools (e.g., Spark, Hadoop).
Strong problem-solving skills and the ability to communicate complex technical concepts to non-technical stakeholders.
Hands-on experience with the modern data stack: Snowflake, Hex, and cloud platforms (AWS/GCP).

Senior Data Scientist

Key skills