Senior Data Scientist
J
JobgetherAI and machine learning
Based in GermanyFull-TimeSenior
Salary not disclosed
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Job Details
- Required Skills
- PythonSQLMachine LearningNumpyPandasscikit-learnMLOps
Requirements
- Experience leading, developing, and owning AI or data science projects from scoping through implementation and ongoing improvement.
- Practical knowledge of machine learning techniques and large language models (LLMs), including real-world applications.
- Advanced Python programming skills, including Pandas, NumPy, and Scikit-learn.
- SQL skills for querying, aggregating, and joining complex datasets.
- Experience transforming messy, high-volume data into structured datasets for analytical and predictive modeling.
- Understanding of feature extraction, selection, and transformation techniques.
- Foundation in statistics, probability, and applied machine learning, including model evaluation and validation.
- Experience translating business or product requirements into analytical solutions and delivering work under tight deadlines.
- Familiarity with production machine learning deployment and monitoring, including MLOps practices and workflows.
- Experience with AWS services, particularly SageMaker and Amazon S3, or comparable cloud-based machine learning infrastructure.
- Ability to collaborate across engineering, product, and data teams.
Responsibilities
- Lead the development, implementation, maintenance, and improvement of machine learning, LLM, and automation projects.
- Process, clean, transform, and analyze large-scale network and device telemetry datasets.
- Design feature engineering strategies to improve model accuracy, interpretability, robustness, and performance.
- Use Python and SQL to explore datasets, build analytical pipelines, and produce reproducible results.
- Develop and evaluate models for anomaly detection, forecasting, and system optimization.
- Translate product and business needs into analytical problems, project scopes, and model development plans.
- Collaborate with engineering and product teams to integrate models into production systems.
- Contribute to MLOps workflows, including deployment, validation, monitoring, and performance improvement.
- Identify opportunities to apply AI and automation to operational efficiency and connected-device experiences.
- Communicate findings, technical approaches, and model outcomes to cross-functional stakeholders.
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