Data Science Professional
4 days ago
We are seeking a highly motivated professional to develop and maintain data pipelines, implement machine learning models, and work closely with our engineers to solve complex problems.
Key Responsibilities:
- Data Pipeline Development: Build and automate Python pipelines to convert raw data into clean, analysis-ready datasets.
- Data Storage & Management: Organize and maintain structured datasets (CSV, SQL) with proper versioning and backups.
- Feature Engineering & ML Support: Extract and validate key features to support ML model training.
- Visualization & Reporting: Create clear plots, dashboards, and automated reports for individual dataset and campaign summaries.
- Data Quality Control: Ensure all datasets meet QC standards before model fitting by applying data health checks, handling missing data, and enforcing unit consistency.
- Metadata Tracking: Maintain run index files linking each test with conditions, parameters, and operator notes.
- Documentation: Write and maintain clear documentation of data pipelines, workflows, and processing standards.
- Tooling & Automation: Develop helper scripts and utilities to streamline batch processing, parameter fitting, and validation.
- Research Support: Provide clean, well-structured datasets and analysis outputs for physics model calibration, ML experiments, and publications.
Requirements:
- Bachelor's degree in engineering or related field.
- 0-2 years of experience in data pipeline development and applied ML.
- Strong theoretical knowledge and hands-on experience in python, ML, AI.
- Strong programming skills in Python (writing clean, modular, well-documented code).
- Proficiency in pandas, NumPy for large-scale data wrangling.
- Familiarity with ML workflows: preprocessing, train/test split, feature scaling, cross-validation.
- Good at data visualization & exploratory analysis (Matplotlib, Plotly, Seaborn).
- Good problem-solving mindset with a balance between practical coding and scientific thinking.
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