Senior ML Back-end Engineer

We are looking for a Junior Data Scientist with a strong engineering mindset who is eager to evolve beyond "notebook data science." In this role, you will work across the full consumer finance lifecycle—analyzing data for Growth, Pricing, and Risk—while being trained on a modern, high-performance MLOps stack. You will learn to treat data science code as production software.

Key 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).

Benefits:
Office environment: When you come to our b_labs office, you'll find creative workspaces and an open design to foster collaboration between teams.

Flexibility: You know best whether you want to work from home or in the office.

Equipment: From "Day 1" you will receive all the equipment you need be successful at work.