Data Scientist II – Model Validation and Monitoring

Vytwo; Prosper (TX); On-site; USD 90,000 - 120,000; Full time

Job summary

Vytwo is seeking a Data Scientist II for their Model Validation and Monitoring Team, based in located in Prosper, Texas. The successful candidate will lead model monitoring activities and conduct model validation to ensure compliance with business and regulatory standards. They should possess significant experience in data science and be proficient in Python, R, and SQL while also being able to effectively communicate complex analyses to non-technical audiences. This is an onsite position with a focus on delivering machine learning models.

Qualifications

• Minimum of 2 years of data science or related experience required.
• Experience developing data pipelines in Python, R, or equivalent.
• Proficiency in writing and tuning SQL queries.
• Ability to handle terabyte size datasets using Spark.
• Experience using machine learning libraries like mllib.
• Strong communication skills for non-technical audiences.

Responsibilities

• Lead model monitoring and performance metrics tracking.
• Conduct rigorous model validations during development phases.
• Explore and aggregate data to identify anomalies impacting performance.
• Write production-level code in a dynamic environment.
• Solve complex problems using large datasets.
• Partner with cross-functional teams to identify trends.

Skills

Machine Learning Techniques

Data Visualization

Python Programming

SQL

Data Science Pipelines

Root Cause Analysis

Tools

scikit-learn

Spark

PySpark

Job description

Role : Data Scientist II – Model Validation and Monitoring

Location: Scottsdale AZ (Onsite)

*US Citizen & GC Only

**Must be legally authorized to work in US without need for employer sponsorship now or at any time in the future.

Overall, Purpose

This position serves as a data science team member in the Model Validation and Monitoring Team delivering leading edge machine learning models to our clients. This includes providing effective challenges to model development, conducting model monitoring and performance tracking, providing root cause analysis of model performance, exploring, building, validating, and deploying models.

Essential Functions

• Lead model monitoring activities, including tracking performance metrics, detecting model and data drift, identifying data quality issues, providing root cause analysis, and recommending remediation strategies.
• Conduct rigorous model validation by providing effective challenges during model development phases, including performance testing, benchmarking, providing remediation plan, and documentation to ensure models meet business, technical, and regulatory standards.
• Explore and aggregate data independently to uncover data anomalies that impact algorithm performance.
• Write production level code in a dynamic, start-up environment.
• Solve complex problems using terabyte size data sets.
• Apply a variety of machine learning techniques to a business problem to arrive at optimal approach.
• Partner with Product and Engineering teams to solve problems and identify trends and opportunities.
• Explain and visualize results and algorithm performance to non-technical audiences.

Minimum Qualifications

• A minimum of 2 years of data science, engineering, mathematics, or related work experience is required.
• Experience developing data science pipelines & workflows in Python, R or equivalent programming language. Experience in writing and tuning SQL. Experience handling terabyte size datasets with Spark language.
• Experience applying various machine learning techniques, and understanding the key parameters that affect model performance.
• Experience using ML libraries, such as scikit-learn, mllib, etc.
• Experience using data visualization tools.
• Able to write production level code, which is well‑written and explainable.
• Ability to effectively communicate findings from complex analyses to non-technical audiences.

Preferred Qualifications

• Experience using advanced ML algorithms building, testing, and deploying fraud models.
• Hands‑on experience with PySpark.
• Industry experience in building or validating machine learning models.
• Experience exploring data and finding hidden patterns and data anomalies.