Machine Learning Engineer / Data Scientist

Role Overview

Machine Learning Engineer / Data Scientist to build and deploy production-ready ML solutions. You’ll own work across the full ML lifecycle—turning ambiguous business problems into modeling approaches, delivering trained models, and monitoring performance in the real world.

Responsibilities

• Translate business challenges into ML tasks (classification, regression, forecasting, clustering, anomaly detection).
• Collaborate with stakeholders to define success metrics, constraints, and evaluation strategies.
• Extract, clean, and analyze data using Python and SQL, ensuring data quality and modeling readiness.
• Build feature engineering pipelines (transformations, encoding, scaling, aggregation).
• Develop, tune, and validate models for supervised, unsupervised, and time-series use cases.
• Apply deep learning when appropriate using PyTorch or TensorFlow/Keras.
• Perform model evaluation and error/interpretability analysis using metrics, SHAP, and cohort-based insights.
• Support deployment efforts via APIs and/or batch pipelines; contribute to MLOps (monitoring, retraining).
• Communicate results, trade-offs, and recommendations to technical and non-technical stakeholders.
• Document methods, assumptions, and results for reproducibility and transparency.

Requirements

• 3–8 years of experience in data science, ML engineering, or applied ML.
• Strong Python skills for data analysis and modeling (pandas, NumPy, scikit-learn or equivalent).
• Advanced SQL (joins, window functions, performance-aware querying).
• Solid statistics, experimentation, and probabilistic reasoning.
• Hands-on experience with classification, regression, time series forecasting, and clustering.
• Deep learning experience with PyTorch or TensorFlow/Keras.
• Ability to work with messy/ambiguous datasets and turn them into structured ML solutions.
• Strong communication skills.

Preferred

• Databricks and cloud platforms (AWS/GCP/Azure).
• Orchestration tools (Airflow, Prefect, Dagster).
• MLOps workflows; experience with production deployment, monitoring, and retraining pipelines.

Location / Eligibility

• Based in the United States and able to work there without immigration sponsorship.