We are looking for a Data Scientist with strong experience in Machine Learning Engineering.
The candidate will be responsible for designing, developing, and maintaining advanced analytics models. They will ensure quality and compliance with the client’s MLOps best practices by implementing scalable, reliable, and reproducible ML solutions on cloud platforms. Experience in automating the ML lifecycle and continuously improving model performance will be highly valued.
Requirements
- Education: Engineering, Computer Science, Mathematics, Statistics, or a related field.
- Experience: Minimum of 3 years applying ML in production environments.
- Programming: Python (PEP8, Pylint, Black) and adherence to clean coding standards.
- Modeling: Design and training of supervised and unsupervised models.
- MLOps: Data preparation, feature engineering, training, registration, deployment, and monitoring.
- Tools: Databricks for analytics and ML; MLflow for model lifecycle management.
- DevOps: Azure DevOps (Gitflow, CI/CD, version control).
- Best Practices: Model and pipeline testing, environment management (dev, prod).
- Performance: Ability to configure and track metrics (MAE, RMSE, F1, AUC-ROC), anomaly detection, and production alerts.
- Collaboration: Strong communication skills to work with multidisciplinary teams and convey results to both technical and non-technical stakeholders.
Project and Role
The role involves developing, deploying, and maintaining ML models on cloud platforms using Databricks, MLflow, and Azure DevOps. The goal is to build reproducible, monitored, and well-governed analytics solutions that meet the client’s MLOps best practices. The main focus is automating the ML lifecycle—from data preparation and feature engineering to training, registration, deployment, and monitoring in production. Experience designing scalable, resilient, and maintainable solutions aligned with business goals and performance metrics is highly valued.
As a Data Scientist - ML Engineer, you will be responsible for designing and maintaining ML models in production, ensuring quality and compliance with MLOps practices.
- Design, train, and evaluate supervised and unsupervised models, with an emphasis on reproducibility and traceability.
- Develop data and feature pipelines, including data cleaning, transformation, and quality validation.
- Manage the model lifecycle using MLflow: tracking, registration, and versioning of models and metrics.
- Work with Databricks for analytics and ML, optimizing notebooks and shared notebook workflows.
- Oversee deployments and monitoring in Azure DevOps, applying Gitflow practices, CI/CD, and version control.
- Define and follow coding standards (PEP8), implement model and pipeline testing, and manage environments (dev, prod).
- Set up performance metrics, anomaly detection, and alerts for models in production.
- Collaborate with data engineers, tech leads, and other stakeholders to align solutions with business objectives.
- You are expected to demonstrate teamwork, proactivity in problem-solving, and commitment to quality and continuous improvement.
- Knowledge of analytical project frameworks (e.g., Databricks Asset Bundles), advanced cloud architecture, and scalable ML solution design
- Experience with Lakeview or other tools for interactive visualization and monitoring
- Proven experience implementing end-to-end automated ML pipelines
- Ability to manage user feedback to continuously improve models.
