Data Scientist Mid-Level
Tasks
- Apply classification scoring summarization pattern detection and feature generation methods
- Build refine and maintain notebook based reproducible workflows in Databricks and cloud environments
- Collaborate with data analysts AI scientists AI engineers analytics engineers data engineers and architects
- Design and execute segmentation forecasting propensity modeling anomaly detection experimentation and recommendation analyses
- Develop analytical statistical machine learning and applied AI solutions for business use cases
- Document assumptions methodology features model decisions evaluation criteria limitations and findings
- Follow governance privacy and responsible data and AI standards
- Support evaluation and practical application of LLM enabled or AI assisted workflows
- Train tune validate and evaluate machine learning and AI models
- Translate business questions into analytical approaches and modeling strategies
Perks/Benefits
- Collaborative global framework
- Creativity autonomy and diversity culture
- Growth opportunities
- International careers
- Values-driven culture
Skills/Tech-stack
AI Services | Anomaly Detection | Apache Spark | Azure AI | Azure AI Services | Azure DevOps | Azure Machine Learning | BigQuery | Cloud platform | Databricks | Databricks Machine Learning | Experiment design | Feature Engineering | Forecasting | Git | GitHub | Google Cloud | Google Cloud Platform | Jupyter Notebooks | LLM Evaluation | LightGBM | Looker | MLflow | Machine Learning | Matplotlib | Microsoft Azure | Model Evaluation | NumPy | Pandas | Plotly | Power BI | Prompt engineering | Propensity Modeling | PySpark | Python | SQL | SciPy | Scikit-learn | Seaborn | Snowflake | StatsModels | Supervised Learning | Tableau | Unsupervised Learning | Vertex AI | XGBoost
Education
N/A
Roles
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