Principal Data Scientist

🕒 July 16

🇺🇸 United States – Remote

⏰ Full Time

🔴 Lead

📊 Data Scientist

👻 Ghost score 35%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Risepoint

WebsiteLinkedInAll Job Openings

1001 - 5000 employees

Founded 2007

💼 Consulting

📣 Marketing

📚 Education

Consulting • Marketing • Education

Risepoint is an education technology company that partners with regional public and private universities to design, launch, and grow workforce-focused online degree programs. It provides marketing, student support, faculty support, technology implementations, performance monitoring, and student ROI research to help institutions expand access, increase enrollments, and deliver high-return programs for working adults.

📋 Description

• Lead data science and AI/ML initiatives from problem definition through implementation and measurement • Turn ambiguous business problems into clear data science opportunities, analytical approaches, and measurable outcomes • Own the technical direction of assigned initiatives and make recommendations on technical or business tradeoffs • Collaborate with Product, Engineering, Customer Experience, Partnership, and university partner teams to implement data science solutions • Identify opportunities for machine learning, experimentation, causal inference, optimization, or AI to improve student and business outcomes • Communicate findings and recommendations to technical teams, business partners, and senior leaders • Build and improve predictive models for enrollment propensity, retention and churn risk, engagement, student success, personalization, and next best action • Select appropriate modeling approaches, including supervised and unsupervised learning, recommendation methods, causal inference, and uplift modeling • Develop reusable feature engineering and modeling approaches • Work with structured and unstructured data, including CRM, LMS, behavioral, communication, and speech analytics data • Evaluate models using performance, calibration, explainability, and business metrics • Improve deployed models using real-world outcomes and maintain validation, documentation, reproducibility, and responsible-use standards • Design and lead A/B tests and define success measures with Product and business teams • Translate experiment results into recommendations to launch, change, expand, or stop an approach • Build production-ready data science solutions using Python, SQL, Databricks, MLflow, and related technologies • Partner with Data Engineering and Engineering to productionize models and ensure reliable data and feature pipelines • Define model monitoring requirements for performance, data quality, drift, and business outcomes • Maintain testing, versioning, documentation, and governance throughout the model lifecycle • Investigate production model issues and resolve data, pipeline, or serving problems with engineering partners • Contribute to automated testing, CI/CD, and model deployment practices • Provide technical leadership and mentor data scientists through reviews, pair work, and knowledge sharing • Establish standards for model development, experimentation, validation, documentation, explainability, and responsible AI • Introduce methods and tools that improve team effectiveness • Help shape the Data Science and AI roadmap and inform technical priorities

🎯 Requirements

• 8+ years of experience in applied data science, machine learning, or a related field, including technical leadership on complex projects • Strong hands-on experience building predictive models such as propensity, churn, retention, engagement, personalization, recommendation, or other behavioral models • Strong understanding of machine learning, statistics, feature engineering, and model evaluation • Experience designing and analyzing A/B tests used to make real product or business decisions • Experience with causal inference, uplift modeling, incrementality measurement, or related methods • Strong Python and SQL skills and experience with common ML libraries such as scikit-learn, XGBoost, TensorFlow, or PyTorch • Experience taking machine learning models into production and monitoring their performance after launch • Experience working with Data Engineering or Engineering teams to productionize data science solutions • Experience with modern data science and MLOps platforms such as Databricks and MLflow, or comparable technologies • Experience working with large, complex datasets from multiple sources • Strong judgment about data quality, model validation, monitoring, explainability, and responsible ML • Experience mentoring data scientists and providing technical direction on projects • Ability to turn complex technical work into clear recommendations for Product, Engineering, business partners, and senior leaders • Bachelor's or Master's degree in computer science, statistics, econometrics, mathematics, engineering, or another quantitative discipline, or equivalent relevant experience • PhD in a quantitative or technical discipline (great to have) • Experience in higher education, edtech, consumer technology, personalization, or another complex behavioral domain (great to have) • Experience building recommendation systems, next-best-action solutions, propensity models, or personalization systems (great to have) • Advanced experience with causal inference, uplift modeling, or treatment-effect estimation (great to have) • Experience with speech analytics, NLP, conversational data, or other unstructured behavioral data (great to have) • Experience integrating ML solutions with CRM, LMS, customer engagement, or marketing platforms (great to have) • Familiarity with responsible AI practices, including model transparency, fairness, and bias evaluation (great to have) • Experience with AI-assisted data science and software development tools (great to have)

🏖️ Benefits

• Equal-opportunity employer • Diverse and inclusive workforce