Data Scientist

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.