Sr ML Engineer

Sonatafy Technology

1d•Remote

About The Position

Sonatafy Technology, headquartered in Scottsdale, Arizona, is an award-winning nearshore software development company with a strong reputation. They have a dedicated in-house team of engineers, offering end-to-end software solutions and supporting client development staff augmentation. Catering to companies of all sizes and industries, including some of the world's largest brands, Sonatafy Technology is a trusted provider of nearshore enterprise-level cloud and mobile application software development services. This opening is available for candidates in Latin America, not limited to only Mexico.

Requirements

5+ years of experience in Data Analytics, Data Science, or a combined analytics and ML role.
Demonstrated experience building and deploying ML models in a business context, not just academic or exploratory work.
Strong analytical and problem-solving mindset with the ability to translate complex data into clear, actionable insight.
Excellent communication skills to work effectively with both technical and non-technical stakeholders.
Python proficiency for both data wrangling and ML model development: Data libraries: pandas, NumPy, matplotlib, seaborn ML libraries: scikit-learn, XGBoost, LightGBM, or equivalent Model evaluation and validation: cross-validation, train/test splits, hyperparameter tuning
Strong SQL skills including querying, optimization, and data modeling.
Experience with dbt for building and version-controlling data transformations.
Solid understanding of relational databases and data warehouse concepts.
Comfort with statistical reasoning: distributions, hypothesis testing, regression, and uncertainty quantification.

Nice To Haves

Cloud data warehouses: Hands-on experience with Google BigQuery, Snowflake, or Redshift for querying, modeling, and analytics.
MLOps familiarity: Exposure to experiment tracking tools such as MLflow, Weights and Biases, or similar.
Government data: Experience working with U.S. government datasets and understanding of related data governance requirements.
Deployment exposure: Experience with lightweight model deployment patterns (APIs, batch scoring, or embedded model outputs in dashboards).
Deep learning: Familiarity with deep learning frameworks such as PyTorch or TensorFlow for use cases that go beyond classical ML.

Responsibilities

Analyze large datasets to surface trends, patterns, and actionable business insights.
Build and maintain data models, transformations, and pipelines using SQL and dbt.
Collaborate with stakeholders to define metrics, KPIs, and reporting requirements.
Support data governance, ensuring data quality, integrity, and accessibility across the organization.
Document processes, workflows, and analysis outcomes for cross-functional teams.
Work with U.S. government-related datasets and provide domain-specific insights (preferred experience).
Design, build, and evaluate supervised and unsupervised ML models (classification, regression, clustering, forecasting).
Lead problem framing conversations with stakeholders to translate business questions into ML-ready problem statements.
Conduct feature engineering, selection, and transformation to prepare data for model training.
Validate and communicate model performance using appropriate evaluation metrics (AUC, RMSE, F1, precision/recall, etc.).
Support lightweight model deployment and monitoring, flagging performance drift and recommending retraining triggers.
Contribute to experiment design and A/B testing frameworks where applicable.