Data Scientist

JOHNSON SERVICE GROUP; Houston (TX); On-site; USD 138,000 - 158,000; Part time

Job summary

Johnson Service Group (JSG) is seeking a Data Scientist (Models) in Houston to develop predictive models for BOM/configuration mix and net manufacturing demand. This on-site, 12-week contract involves feature engineering and model experimentation alongside a Lead Data Scientist.

You will build classifiers, develop parallel model tracks, and document models with model cards and experiment logs to ensure traceability and accuracy reports.

Qualifications

• ML modeling experience across classification and regression.
• Strong Python skills with scikit-learn and LightGBM/XGBoost.
• AWS SageMaker experience, including training jobs and hyperparameter tuning.
• Ability to work under the direction of a Lead Data Scientist while independently supporting separate model development tracks.
• Strong technical documentation practices, including model cards, experiment logs, and accuracy reports.

Responsibilities

• Build a BOM/configuration mix classifier to predict series and part-number mix from family-group-level demand.
• Develop models in parallel with the Lead Data Scientist across separate model tracks to avoid single-threaded development.
• Develop a net demand model incorporating salvage, rerun, and repair signals to calculate total manufacturing need.
• Implement feature engineering pipelines designed by the Lead Data Scientist.
• Conduct model experimentation, including hyperparameter tuning, ablation studies, and feature importance analysis.
• Create and maintain model cards, experiment logs, accuracy reports, and related technical documentation.

Skills

Machine learning

Python (scikit-learn)

Python (LightGBM/XGBoost)

AWS SageMaker

Independent modeling

Model documentation

Job description

Johnson Service Group (JSG) is currently looking for a Data Scientist (Models) with machine learning model development experience. Our client is seeking a Data Scientist to develop predictive models supporting BOM/configuration mix and net manufacturing demand while executing feature engineering and model experimentation. Houston- On site Must Have skills Machine learning modeling experience across classification and regression. Strong Python skills with scikit-learn and LightGBM/XGBoost. AWS SageMaker experience, including training jobs and hyperparameter tuning. Ability to work effectively under the direction of a Lead Data Scientist while independently supporting separate model development tracks. Strong technical documentation practices, including model cards, experiment logs, and accuracy reports.

Must Have skills

• Machine learning modeling experience across classification and regression.
• Strong Python skills with scikit-learn and LightGBM/XGBoost.
• AWS SageMaker experience, including training jobs and hyperparameter tuning.
• Ability to work effectively under the direction of a Lead Data Scientist while independently supporting separate model development tracks.
• Strong technical documentation practices, including model cards, experiment logs, and accuracy reports.

Nice to Have skills

• Multi-class classification experience.
• Exposure to manufacturing and inventory data.

Responsibilities

• Build a BOM/configuration mix classifier to predict series and part-number mix from family-group-level demand.
• Develop models in parallel with the Lead Data Scientist across separate model tracks to avoid single-threaded development.
• Develop a net demand model incorporating salvage, rerun, and repair signals to calculate total manufacturing need.
• Implement feature engineering pipelines designed by the Lead Data Scientist.
• Conduct model experimentation, including hyperparameter tuning, ablation studies, and feature importance analysis.
• Create and maintain model cards, experiment logs, accuracy reports, and related technical documentation.

Other Information

• Contract Length: 12 weeks.
• Contract Rate: $100 - $115USD

Johnson Service Group (JSG) is an Equal Opportunity Employer. JSG provides equal employment opportunities to all applicants and employees without regard to race, color, religion, sex, age, sexual orientation, gender identity, national origin, disability, marital status, protected veteran status, or any other characteristic protected by law.

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