Senior ML Engineer
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Job Description
Senior ML Engineer analyzing large datasets and building machine learning models for Sonatafy Technology. Collaborating with stakeholders in a fast-paced, client-first culture.
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.
- 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.
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.
- 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.
Benefits:
- competitive compensation
- remote-first lifestyle
- career growth opportunities across industries and technologies
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