Machine Learning Scientist

Role Overview

Sundayy is hiring a Machine Learning Scientist for its Machine Learning and Data Platform team. This is a foundational role where you’ll design, develop, and deploy scalable machine learning solutions in collaboration with cross-functional stakeholders, shaping data pipelines and MLOps practices to deliver robust and reproducible outcomes.

Responsibilities

• Design, develop, and implement machine learning algorithms in production (EDA, data modeling, feature engineering, training/tuning, testing, deployment, and monitoring).
• Partner with cross-functional teams to identify data-driven improvement opportunities and influence strategic decisions.
• Build scalable ML systems that improve user experience with intelligent features.
• Contribute across the ML lifecycle (idea generation, opportunity assessment, prototyping, testing, and deployment).
• Design and build end-to-end data pipelines that support MLOps and business processes.
• Create and maintain dashboards, notebooks, documentation, and presentations for technical and non-technical audiences.
• Promote best practices in an experimentation-driven culture to ensure reproducible, high-quality work.

Requirements

• 5+ years of industry experience applying scientific methods to real-world problems using consumer-scale data.
• Proficiency in Python and SQL for data science and ML development.
• Experience with key libraries and tooling: NumPy, SciPy, pandas, scikit-learn, PyTorch, LightGBM, Flask, FastAPI, Docker, Jupyter.
• Production experience deploying ML/data solutions.
• Strong understanding of data warehouses and transformation tools: Snowflake, dbt, Dagster.
• Applied expertise in statistics/ML such as experimentation, causal analysis, recommendations, fraud detection, NLP, or computer vision.
• Ability to communicate insights using visualization tools (dashboards, notebooks).
• Strong collaboration, documentation, and communication skills in remote environments.

Nice to Haves

• Prior experience across multiple applied ML domains (e.g., fraud detection, NLP, computer vision) and experimentation/causal inference workflows.