Data Scientist enhancing merchandising and inventory performance using machine learning techniques at Nordstrom. Collaborating with teams to develop data - driven solutions for better customer experiences.
About the role
- Junior Data Scientist at fintech platform mylo, analyzing data across Growth, Pricing, and Risk. Collaborating with engineering teams to develop models and ensure data quality for real-time applications.
Responsibilities
- End-to-End Modeling: Assist in training and tuning models for various business domains using modern Python libraries.
- Engineering Integration: Work with the team to expose models via APIs. You will learn to implement Feature Store definitions and ensure data quality for real-time serving.
- Data Operations: Handle data preparation and analysis using SQL and Python. Learn to manage datasets using Data Version Control tools to keep track of changes.
- Code Quality: Write clean, modular, and tested code. You will participate in code reviews and use version control (Git) as part of your daily workflow.
- Continuous Learning: Participate in our induction program to master our specific tools for model serving, package management, and system monitoring.
Requirements
- Education: B.Sc. in Computer Science / Engineering, Statistics, Mathematics, or a relevant quantitative field.
- Technical ML Foundation:
- Algorithms: Solid conceptual and practical understanding of Classification (Logistic Regression, Decision Trees, Random Forests) and Regression analysis.
- Deep Learning: Basic understanding of Neural Networks architectures and principles (e.g., activation functions, loss functions, backpropagation).
- Libraries: Hands-on familiarity with Scikit-Learn for preprocessing, model selection, and pipelines.
- Optimization: Exposure to hyperparameter tuning concepts and gradient boosting frameworks (e.g., LightGBM or XGBoost).
- Software Engineering Fundamentals:
- Version Control: Strong familiarity with Git commands (branching, merging, resolving conflicts) and collaboration platforms (GitHub/GitLab).
- Code Quality: Ability to write clean, reusable, and readable code (not just scripts). Understanding of functions, modularity, and basic testing.
- Core Skills: Strong grasp of Python programming and SQL.
- Analytical Foundation: Solid understanding of statistics and standard data manipulation libraries (Pandas, NumPy).
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