Manipulate, clean, and transform large-scale datasets of network and device telemetry for modeling and analysis.
Design and implement feature engineering strategies that enhance model accuracy, interpretability, and robustness.
Use SQL and Python (Pandas, NumPy, Scikit-learn, etc.) to explore data, build analytical pipelines, and deliver reproducible results.
Translate product or business needs into well-scoped analytical tasks and model development plans.
Build and evaluate machine learning models for anomaly detection, forecasting, and optimization.

Ключевые требования

Manipulate, clean, and transform large-scale datasets of network and device telemetry for modeling and analysis.
Design and implement feature engineering strategies that enhance model accuracy, interpretability, and robustness.
Use SQL and Python (Pandas, NumPy, Scikit-learn, etc.) to explore data, build analytical pipelines, and deliver reproducible results.
Translate product or business needs into well-scoped analytical tasks and model development plans.
Build and evaluate machine learning models for anomaly detection, forecasting, and optimization.

Вакансии в Plume

Открытые роли в этой компании — 1 в каталоге JobMatcher.

Все вакансии компании

Вакансии в Plume

Открытые роли в этой компании — 1 в каталоге JobMatcher.

Все вакансии компании

About the role

Plume is seeking a hands-on, impact-oriented Data Scientist who thrives on solving complex problems with data. You will design, build, and deploy data-driven models that extract insights from network traffic and telemetry data, while shaping the features, datasets, and pipelines that power those models. You will translate business and technical requirements into concrete data analyses, experiments, and production-ready solutions, often under tight delivery timelines.

Responsibilities

Qualifications:

Manipulate, clean, and transform large-scale datasets of network and device telemetry for modeling and analysis.
Design and implement feature engineering strategies that enhance model accuracy, interpretability, and robustness.
Use SQL and Python (Pandas, NumPy, Scikit-learn, etc.) to explore data, build analytical pipelines, and deliver reproducible results.
Translate product or business needs into well-scoped analytical tasks and model development plans.
Build and evaluate machine learning models for anomaly detection, forecasting, and optimization.
Collaborate closely with engineering and product teams to integrate models into production systems and monitor their performance.
Balance experimentation with execution—delivering insights and models efficiently under tight schedules.
Contribute to MLOps workflows for model deployment, validation, and monitoring.
Strong programming skills in Python and experience with core data science libraries.
Solid SQL skills for querying, aggregating, and joining complex datasets.
Proven experience transforming messy,

About the role

Plume is seeking a hands-on, impact-oriented Data Scientist who thrives on solving complex problems with data. You will design, build, and deploy data-driven models that extract insights from network traffic and telemetry data, while shaping the features, datasets, and pipelines that power those models. You will translate business and technical requirements into concrete data analyses, experiments, and production-ready solutions, often under tight delivery timelines.

Responsibilities

Qualifications:

Manipulate, clean, and transform large-scale datasets of network and device telemetry for modeling and analysis.
Design and implement feature engineering strategies that enhance model accuracy, interpretability, and robustness.
Use SQL and Python (Pandas, NumPy, Scikit-learn, etc.) to explore data, build analytical pipelines, and deliver reproducible results.
Translate product or business needs into well-scoped analytical tasks and model development plans.
Build and evaluate machine learning models for anomaly detection, forecasting, and optimization.
Collaborate closely with engineering and product teams to integrate models into production systems and monitor their performance.
Balance experimentation with execution—delivering insights and models efficiently under tight schedules.
Contribute to MLOps workflows for model deployment, validation, and monitoring.
Strong programming skills in Python and experience with core data science libraries.
Solid SQL skills for querying, aggregating, and joining complex datasets.
Proven experience transforming messy,

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