Senior Data Scientist

ClickUp

1d•Remote

About The Position

At ClickUp, we’re not just building software. We’re architecting the future of work! In a world overwhelmed by work sprawl, we saw a better way. That’s why we created the first truly converged AI workspace, unifying tasks, docs, chat, calendar, and enterprise search, all supercharged by context-driven AI, empowering millions of teams to break free from silos, reclaim their time, and unlock new levels of productivity. At ClickUp, you’ll have the opportunity to learn, use, and pioneer AI in ways that shape not only our product, but the future of work itself. Join us and be part of a bold, innovative team that’s redefining what’s possible! 🚀 ClickUp is seeking a technical expert in recommendation systems and advanced deep learning that has strengths in architecting comprehensive, end-to-end machine learning pipelines and deployment strategies. This individual has a strong and balanced understanding of model architectures such as Graph Neural Networks (GNN) and Transformers, and is well versed in modern MLOps practices. The ideal Senior Data Scientist understands the mechanics of Product-Led Growth (PLG) and has prior experience working with Product, Analytics, and Engineering teams to build Next Best engines and intelligent personalization features. You are an innovative technical thinker who makes impactful decisions supported by rigorous experimentation and large-scale data analysis. If you're a curious, precise, and impact-driven go-getter that thrives when faced with complex technical challenges...this role is meant for you!

Requirements

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).
Solid understanding of MLOps best practices and CI/CD for machine learning (MLflow, Docker, Kubernetes).

Responsibilities

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.