Senior Data Scientist - Real-Time ML (Hybrid, NYC)

Orbis Group; New York (NY); Hybrid; USD 180,000 - 240,000; Full time

Job summary

Orbis Group in New York is seeking a Senior Data Scientist to help build real-time machine learning systems at scale in a hybrid work model.

You will progress core ML models, partner with customers to drive outcomes, and contribute to production ML activities such as feature generation, training, and monitoring. Strong communication across technical and non-technical audiences is essential.

Qualifications

• 8+ years of experience as an individual contributor in Applied Fraud Research, Data Science, or Machine Learning, with client-oriented roles
• Experience handling highly imbalanced datasets and production-level ML models, especially tree-based methods
• Strong Python and SQL scripting skills
• Experience analyzing large-scale data (billions of records)
• Background creating metrics and dashboards
• Ability to clearly communicate results to technical and non-technical audiences
• Values: take risks, be resourceful, collaborate, and embrace diversity
• Experience in analytical roles within dynamic settings
• Must reside in the Greater New York City area

Responsibilities

• Participate in the creation, development, and assessment of machine learning models
• Create performance reports, metrics, and testing strategies, and translate results into actionable recommendations
• Support production ML activities, such as feature generation, model training, and monitoring
• Record findings and share insights with internal teams for shared learning
• Maintain model documentation and assist model governance procedures
• Keep up with industry trends in machine learning

Skills

Python

SQL

Machine Learning

Feature Engineering

Supervised Learning

Model Evaluation

Data Analysis

Cross-functional Collaboration

Communication

Problem Solving

Education

Masters or PhD in quantitative discipline

Tools

Looker

Job description

Orbis Group in New York is seeking a Senior Data Scientist to help build real-time machine learning systems at scale in a hybrid work model.

You will progress core ML models, partner with customers to drive outcomes, and contribute to production ML activities such as feature generation, training, and monitoring. Strong communication across technical and non-technical audiences is essential.