Machine Learning Engineer / Data Scientist
New
Based in the United StatesFull-TimeMiddle
Salary not disclosed
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Job Details
- Experience
- 3–8 years
- Required Skills
- PythonSQLKerasMachine LearningNumpyPyTorchPandasTensorflowscikit-learn
Requirements
- 3–8 years of experience in data science, machine learning engineering, or applied ML roles.
- Strong proficiency in Python for data analysis and modeling (pandas, NumPy, scikit-learn or equivalent).
- Advanced SQL skills including joins, window functions, and performance-aware querying.
- Solid foundation in statistics, experimentation, and probabilistic reasoning.
- Hands-on experience with classification, regression, time series forecasting, and clustering techniques.
- Experience with deep learning frameworks such as PyTorch or TensorFlow/Keras.
- Ability to work with messy, ambiguous datasets and translate them into structured ML solutions.
- Strong communication skills with the ability to explain complex results in simple, actionable terms.
- Preferred: experience with Databricks, cloud platforms (AWS/GCP/Azure), orchestration tools (Airflow, Prefect, Dagster), and MLOps workflows.
- Preferred: exposure to production deployment, model monitoring, and retraining pipelines.
Responsibilities
- Translate business challenges into machine learning problems such as classification, regression, forecasting, clustering, and anomaly detection.
- Collaborate with stakeholders to define success metrics, constraints, and evaluation strategies aligned with business goals.
- Extract, clean, and analyze data using Python and SQL, ensuring data quality, consistency, and readiness for modeling.
- Design and build robust feature engineering pipelines, including transformations, encoding, scaling, and aggregation logic.
- Develop, tune, and validate machine learning models across supervised, unsupervised, and time series use cases.
- Apply deep learning techniques using frameworks such as PyTorch or TensorFlow/Keras when appropriate.
- Perform model evaluation, error analysis, and interpretability analysis using metrics, SHAP, and cohort-based insights.
- Support deployment efforts by collaborating on APIs or batch pipelines and contributing to MLOps practices such as monitoring and retraining.
- Communicate findings, trade-offs, and recommendations clearly to both technical and non-technical stakeholders.
- Document methodologies, assumptions, and results to ensure reproducibility and transparency.
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