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.
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.
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).