Data Scientist - Risk & Pricing Models with Revenue Impact

GRiT Solutions; United States; Remote; USD 120,000 - 190,000; Full time

Benefits offered by this job

Continuous Training and Certifications

GRiT Prizes

Exclusive Partnerships/Discounts

Unique Events

Welcoming Office

Competitive Compensation

Pay for Performance

Job summary

GRiT is seeking an experienced Data Scientist to build models that directly impact revenue, risk, and commercial decision making. This role is hands-on, collaborating across risk, conversion, and engagement to support pricing and targeting with a focus on financial outcomes.

The work model is to be defined. Ideal candidates will have strong Python/SQL skills, experience with regression/classification/clustering models, and a background in applying models to production pipelines, including MLflow

Qualifications

• Strong command of Python and SQL for building and maintaining modelling datasets.
• Solid experience developing regression, classification and clustering models.
• Good understanding of financial risk, ROI and business trade offs.
• Advanced English, both written and spoken.
• Practical experience integrating models into production pipelines, using tools such as MLflow or Airflow.

Responsibilities

• Build models with direct impact on revenue, risk and commercial decision making.
• Collaborate across risk, conversion and engagement to support pricing and targeting decisions.
• Operate with a focus on delivering measurable financial outcomes in production environments.

Skills

Python

SQL

Regression models

Classification models

Clustering models

Financial risk

English

ML pipelines

Tools

MLflow

Airflow

Job description

GRiT is seeking an experienced Data Scientist to build models that directly impact revenue, risk, and commercial decision making. This role is hands-on, collaborating across risk, conversion, and engagement to support pricing and targeting with a focus on financial outcomes.

The work model is to be defined. Ideal candidates will have strong Python/SQL skills, experience with regression/classification/clustering models, and a background in applying models to production pipelines, including MLflow