Data Scientist
Tasks
- Build LLM powered agentic workflows with retrieval augmented generation
- Build and maintain machine learning models
- Build next best action decisioning systems
- Collaborate with business teams to define data science solutions
- Create model cards and methodology documentation
- Design end-to-end machine learning pipelines
- Detect model performance degradation
- Develop customer churn and CLTV models
- Develop fraud detection models
- Evaluate multi armed bandit interventions
- Monitor deployed models for drift and data quality
- Run experiments and communicate findings
- Support A B experiments and uplift testing
- Train and deploy machine learning models
Perks/Benefits
- Course access
- Dental care
- Free office refreshments
- Health insurance
- Meal vouchers
- Multisport card
- Optical insurance
- Paid time off
- Performance bonuses
- Promotions
- Public transport pass
- Teambuilding activities
- Training budget
Skills/Tech-stack
A/B | A/B Testing | Airflow | Anthropic API | Autogen | B testing | Batch inference | Bias Mitigation | BigQuery | Causal Inference | Classification | Clustering | Customer Lifetime Value | Dagster | Data Drift | Data Quality | Fairness | Feature Engineering | Fraud Detection | Function Calling | GCP | Kafka | LIME | Langchain | Lifetime Value | LightGBM | Llamaindex | MLflow | Machine Learning | Machine Learning Pipelines | Memory | Model Deployment | Model Evaluation | Model Explainability | Model Monitoring | Multi-Armed Bandits | NumPy | OpenAI API | Pandas | Prefect | Propensity Modeling | PyTorch | Python | RAG | REST APIs | Regression | Responsible AI | SHAP | SQL | Scikit-learn | Series modeling | Streaming Feature Engineering | Supervised Learning | Survival analysis | TensorFlow | Time Series | Time Series Modeling | Unsupervised Learning | Uplift Modeling | Uplift testing | Weights and Biases | XGBoost
Education
N/A
Roles
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