Full-Stack Data Scientist Machine Learning Engineer

Full-Stack Data Scientist Machine Learning Engineer (MLE)

7+ years' experience

Responsibilities

• Design, develop, and validate machine learning and heuristic models, driving measurable improvements in predictive accuracy and business impact.
• Establish and mature the MLOps framework, spanning model registry and versioning, governed development-to-production promotion, automated retraining, and drift and performance monitoring.
• Design and maintain Airflow orchestration for training, inference, and retraining pipelines.
• Own ongoing model operations: monitor model health, diagnose anomalies, and deliver timely remediation.
• Collaborate with data engineering on feature and scoring pipelines and operationalize models through the serving layer.
• Document model methodology, assumptions, and diagnostics to support governance and peer review.

Must-Have

• 7+ years in applied machine learning or data science, with a demonstrated record of deploying models to production environments.
• Advanced proficiency in Python and modern ML libraries (e.g., scikit-learn, XGBoost, or comparable gradient-boosting frameworks).
• Hands-on MLOps expertise: model registry, development-to-production promotion, monitoring, and reproducible retraining.
• Proficiency in workflow orchestration (Airflow) and cloud services (AWS — S3, IAM, boto3).
• Sound judgment in selecting between statistical, heuristic, and machine learning approaches based on problem context.

Good-to-Have

• Deep grounding in statistics — hypothesis testing, regression, distributional analysis, time-series methods, and uncertainty quantification.
• Structured problem-solving — translating ambiguous business questions into testable, data-driven hypotheses.
• Experimentation and causal inference — experimental design, backtesting, and retrospective validation.
• Stakeholder communication — articulating model behavior and trade-offs to non-technical audiences.
• Exposure to generative AI and agentic workflows, including LLM-assisted analysis.
• Strong software-engineering practices: testing, code review, and CI/CD.