Sr. Data Scientist (Hybrid)
Tasks
- Build anomaly detection and event classification models
- Deploy models and monitor evaluation
- Design retrieval ranking vector search systems
- Develop RAG and agentic AI workflows
- Establish experimentation and evaluation methodologies
- Lead AI system architecture
- Mentor junior scientists
- Operate large-scale production systems
- Own end-to-end ML pipelines
- Run A B testing and causal inference
Perks/Benefits
- Competitive vacation and holidays
- Comprehensive wellness programs
- Employee networks
- Hybrid work schedule
- Paid adoption leave
- Paid parental leave
- Professional development opportunities
- Volunteer opportunities
Skills/Tech-stack
A/B | A/B Testing | Agent systems | Agentic AI | Anomaly Detection | B testing | Causal Inference | Deep learning | Detection engineering | Distributed Systems | Embeddings | Experimentation platforms | Feature Stores | Go | Hybrid search | Iceberg | Kafka | Knowledge graphs | Kubernetes | Language Models | Language Processing | Large Language Models | ML pipelines | Machine Learning | Model Monitoring | Model Serving | Multi-Agent | Multi-Agent Systems | Natural Language | Natural Language Processing | Parquet | Python | RAG | Ranking Optimization | Ranking Systems | Recommendation Systems | Reinforcement Learning | S3 | SQL | Security analytics | Series modeling | Spark | Threat detection | Time Series | Time Series Modeling | Triage and Investigation | UEBA | Vector Search
Education
Roles
Regions
Countries
States
Cities
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