Data Scientist II: Fraud & Risk ML, Remote

United States Digital Space LLC; United States; Remote; USD 120,000 - 170,000; Full time

Benefits offered by this job

Stock Options

Pension matched up to 8%

Day one health, dental, and optical

Job summary

United States Digital Space LLC is seeking a data-focused professional to join the Applied Decision Science team. You will design and deploy production ML models to assess transaction risk, collaborate with data engineers and other scientists, and automate repetitive tasks to streamline risk management workflows.

The role emphasizes building end-to-end solutions, running experiments, and communicating results to a variety of stakeholders while leveraging Spark, Databricks, and Python in a

Qualifications

• Degree in computer science or a comparable analytical field.
• 3+ years of post-undergrad work experience required.
• Strong verbal and written communication skills.
• Strong machine learning and statistical background.
• Proficient in Python and SQL in a production codebase.
• Experience with distributed analytics and data tooling such as Spark/Databricks.
• On-call shifts, weekend rotation (Fri/Sat/Sun).

Responsibilities

• Partner with the Business Unit Lead to identify gaps in decisioning performance and implement solutions.
• Develop and deploy production ML models to identify fraud in collaboration with team members.
• Create automation to reduce repetitive manual work.
• Run experiments to optimize decisioning strategies balancing complexity and performance.
• Communicate complex ideas to diverse audiences.
• Write production and offline analytical code in Python.
• Work with distributed data pipelines in Spark/Databricks/GCP.

Skills

Machine learning

Python

SQL

Communication

Education

Bachelor's degree in CS or related field

Advanced degree (nice to have)

Tools

Spark

Databricks

Job description

United States Digital Space LLC is seeking a data-focused professional to join the Applied Decision Science team. You will design and deploy production ML models to assess transaction risk, collaborate with data engineers and other scientists, and automate repetitive tasks to streamline risk management workflows.

The role emphasizes building end-to-end solutions, running experiments, and communicating results to a variety of stakeholders while leveraging Spark, Databricks, and Python in a