Data Scientist

CodePath is reprogramming higher education to create the first generation of AI-native engineers, CTOs, and founders. 

We deliver industry-vetted courses and career support centered on the needs of first-generation and low-income students. Our students train with senior engineers, intern at top companies, and rise together to become the tech leaders of tomorrow. 

With 30,000 students and alumni from 700 colleges now working at 2,000 companies, we are reshaping the tech workforce and the industries of the future.

About the Role

Location: Remote, United States 

Role Type: Full-Time 

Reporting to: Lead Data Scientist 

Compensation: $90,000 to $130,000 per year

CodePath is entering a pivotal stage of growth, building the data, analytics, and AI infrastructure that powers our learning platform and supports tens of thousands of students nationwide. We are looking for a highly capable and mission-driven Data Scientist to join our growing Measurement, Evaluation, and Learning (MEL) team and help shape this future.

As a Data Scientist at CodePath, you will work across the full analytics and modeling lifecycle, supporting data pipelines, conducting exploratory analysis, building statistical and machine learning models, and developing the insights that guide organizational strategy. You will collaborate closely with data engineering and cross-functional partners to ensure our data systems, analyses, and models enable student success, operational efficiency, and accurate impact measurement.

This role is ideal for someone who enjoys solving problems in a fast-paced environment, applies clear analytical thinking, and wants to grow by supporting high-impact projects that advance CodePath’s data and AI work.

 

Key Activities

• Impact Measurement: Define, track, and analyze organizational impact, student outcomes, and program performance with MEL leadership 

• Data Modeling & ML: Build and refine statistical and machine learning models for outcomes analyses, forecasting, and decision support 

• Exploratory Analysis: Conduct deep exploratory analyses to surface trends, anomalies, and insights across large datasets

• Dashboards & BI Tools: Develop and maintain dashboards, reports, and visualizations (Tableau, streamlit, or similar) that translate complex results into clear, actionable insights

• Feature Engineering & Data Preparation: Partner with data engineering to develop reliable datasets and features that power modeling and analytics workflows

• Pipeline Support: Support and validate data pipelines to ensure analytical datasets remain accurate, consistent, and well-structured 

• Cross-Functional Work: Collaborate with program, product, and operations teams to understand their data needs and translate them into well-defined analytical questions

• Documentation: Document analytical processes, models, and methodologies to ensure clarity, scaling, and reproducibility

• Continuous Improvement: Identify opportunities to enhance data quality, improve modeling processes, and expand MEL’s modeling and reporting capabilities

 

Key Success Metrics 

• High-quality Analytical Assets: Produces dashboards, Quarto reports, and reusable modules that meet MEL standards, with 90%+ requiring no major revision

• Stronger Impact Measurement: Improves outcomes reporting and forecasting through validated datasets, models, and analyses

• Improved Data Quality & Reliability: Identifies and resolves data issues across pipelines and transformations in partnership with Data Engineering

• Meaningful Modeling Contributions: Builds statistical or ML models that improve decision-making through greater accuracy, clarity, or adoption

 

Qualifications

• 3+ years of professional experience in data science, machine learning, analytics, or a related field

• Strong foundation in statistics, probability, and machine learning algorithms