Data Scientist

Dashly; United States; Remote; USD 80,000 - 120,000; Full time

Job summary

A leading technology company in the United States is seeking a Data Scientist to uncover fraud and assess labor risk by applying statistical analysis and machine learning. The successful candidate will collaborate with engineering and product teams, design models, and analyze large datasets to drive insights. A strong problem-solving mindset and excellent communication skills are essential, and experience in fraud detection is a bonus.

Qualifications

• Strong problem-solving mindset.
• Excellent communication skills.
• Experience in fraud detection, risk modeling, identity mapping, or entity resolution is a plus.

Responsibilities

• Design and implement statistical models and machine learning algorithms to detect fraudulent patterns.
• Analyze large-scale datasets to derive actionable insights.
• Work cross-functionally with teams to align data initiatives with business goals.
• Collaborate with engineering and product teams to bring solutions into production.
• Apply thoughtful experimentation and model evaluation techniques.

Skills

Statistical analysis

Machine learning

Communication skills

Problem-solving mindset

Job description

Overview

As a Data Scientist, you’ll work at the intersection of data and engineering, applying statistical analysis, machine learning, and thoughtful experimentation to uncover fraud and assess labor risk. You should be comfortable working at the intersection of data and engineering, with a strong problem-solving mindset and excellent communication skills. Bonus points if you have experience in fraud detection, risk modeling, identity mapping, or entity resolution.

Responsibilities

• Design and implement statistical models and machine learning algorithms to detect fraudulent patterns.

• Analyze large-scale datasets to derive actionable insights for internal and customer-facing applications.

• Work cross-functionally with leadership, engineering, and users to align data initiatives with business goals.

• Collaborate closely with engineering and product teams to bring data science solutions into production.

• Shape the intelligence layer of our platform by applying thoughtful experimentation and model evaluation techniques.