Applied Scientist — FinTech ML & Fraud Analytics

Grid; Seattle (WA); On-site; USD 120,000 - 140,000; Full time

Benefits offered by this job

Medical

Dental

Vision

401K

Life Insurance

Job summary

Grid is seeking an Applied Scientist to help build and scale our core data products in Seattle. You will work with product, engineering and business leaders to leverage data for fraud detection, risk underwriting, payouts and more.

You will research, develop and deploy models, read papers, and translate findings into production-grade analytics. Strong Python, PyTorch/TensorFlow and SQL skills are required; experience with ML inference and time-series analysis is a plus.

Qualifications

• Bachelor's or master's degree in Statistics, Mathematics, Physics, or Computer Science with a focus on machine learning is required.
• Strong background in statistical inference and machine learning.
• Experience with deep learning techniques, including transformer-based models.
• Proficiency in Python (PyTorch/TensorFlow) and SQL.

Responsibilities

• Research & Analysis: Perform data research and analysis using Grid's proprietary dataset and other sources.
• Model Development: Develop and validate models to support growth, fraud mitigation, and risk control.
• Deployment & Iteration: Iterate on models based on feedback and real-world performance.
• Productionization: Collaborate with data engineers and product managers to translate work into production-grade data products.
• Present Findings: Communicate findings to team members with varying technical depth.
• Foster DS @ Grid: Help build Applied Science and ML as a team and practice at Grid.

Skills

Statistical inference

Model evaluation

Time series analysis

Self starter

Autonomy

Education

Bachelor's or Master's in Statistics, Mathematics, Physics, or Computer Science with ML focus

Tools

Python

PyTorch

TensorFlow

SQL

Job description

Grid is seeking an Applied Scientist to help build and scale our core data products in Seattle. You will work with product, engineering and business leaders to leverage data for fraud detection, risk underwriting, payouts and more.

You will research, develop and deploy models, read papers, and translate findings into production-grade analytics. Strong Python, PyTorch/TensorFlow and SQL skills are required; experience with ML inference and time-series analysis is a plus.