{"id":41594,"url":"https://alion.io/job/risepoint-principal-data-scientist","title":"Principal Data Scientist","company":{"id":5041,"name":"Risepoint","domain":"risepoint.com","url":"https://alion.io/company/risepoint","size_band":"501-1000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":95,"open_postings":8,"ghost_share":0,"stale_share":0.125,"repost_share":0.125,"time_to_fill_p50_days":32,"computed_at":"2026-10-16T05:45:00Z"}},"role":"Data Science","role_family":"Data Science","seniority":"lead","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["United States"],"countries":["US"],"hiring_countries":["US"],"hiring_countries_total":1,"salary":null,"salary_estimate":{"min_usd":133000,"max_usd":230000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":98},"experience_years_min":8,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false},{"name":"XGBoost","optional":false},{"name":"NLP","optional":true},{"name":"Recommender Systems","optional":true}],"status":"live","first_seen_at":"2026-07-21T00:00:00Z","employer_posted_date":"2026-10-05","last_verified_at":"2026-10-17T02:24:27Z","board_verified":true,"closed_at":null,"days_open":88,"trust":{"level":"ok","repost_count":1,"flags":[],"days_open":88},"description":"Risepoint is an education technology company that helps regional universities launch and grow online programs for modern learners. Risepoint supports more than 100 universities and colleges across five countries, with programs concentrated in high-demand fields including nursing, healthcare, teaching, business, technology, and public service. Our suite of products and services supports the full student journey and each university’s long-term goals. Together, we increase access to affordable education that delivers a strong return on investment for learners and meets employer and community needs. Learn more at risepoint.com.\nThe Impact You Will Make\nAs a Principal Data Scientist, you will lead data science and machine learning initiatives that improve outcomes for students, university partners, and Risepoint.\nThis is a hands-on technical leadership role. You will work across the data science lifecycle, from defining the problem and building models to experimentation, production deployment, and measuring business impact. You will also help set the technical direction for the team, mentor other data scientists, and work closely with Product, Engineering, Customer Experience, and Partnership teams.\nHow You Will Bring Our Mission to Life\nWhat You Will Do\nData Science Leadership & Business Impact\nLead data science and AI/ML initiatives from problem definition through implementation and measurement. \nTurn ambiguous business problems into clear data science opportunities, analytical approaches, and measurable outcomes. \nOwn the technical direction of assigned initiatives and make clear recommendations when technical or business tradeoffs arise. \nWork closely with Product, Engineering, Customer Experience, Partnership, and university partner teams to put data science solutions into practice. \nIdentify new opportunities where machine learning, experimentation, causal inference, optimization, or AI can improve student and business outcomes. \nCommunicate findings and recommendations clearly to technical teams, business partners, and senior leaders. \nMachine Learning & Advanced Analytics\nBuild and improve predictive models for areas such as enrollment propensity, retention and churn risk, engagement, student success, personalization, and next best action. \nChoose modeling approaches appropriate to the problem, including supervised and unsupervised learning, recommendation methods, causal inference, and uplift modeling. \nDevelop strong feature engineering and modeling approaches that can be reused across projects. \nWork with a range of structured and unstructured data, including CRM, LMS, behavioral, communication, and speech analytics data. \nEvaluate models using appropriate performance, calibration, explainability, and business metrics. \nUse real-world outcomes to improve models after they are deployed. Maintain strong standards for model validation, documentation, reproducibility, and responsible use of machine learning. \nExperimentation & Measurement\nDesign and lead A/B tests that measure whether model-driven interventions improve enrollment, retention, engagement, and other outcomes. \nWork with Product and business teams to define what should be tested, how success will be measured, and how results will inform decisions. \nUse causal inference, uplift modeling, and related approaches when traditional A/B testing is not practical or when treatment effects vary across groups. \nDistinguish between statistical significance and meaningful business impact. \nTranslate experiment results into clear recommendations about whether to launch, change, expand, or stop an approach. \nProduction ML\nBuild production-ready data science solutions using Python, SQL, Databricks, MLflow, and related technologies. \nPartner with Data Engineering and Engineering teams to move models into production and make sure the required data and feature pipelines are reliable. \nDefine model monitoring requirements for performance, data quality, drift, and business outcomes. \nMaintain appropriate testing, versioning, documentation, and governance throughout the model lifecycle. \nInvestigate production model issues and work with engineering partners to resolve data, pipeline, or serving problems. \nContribute to automated testing, CI/CD, and model deployment practices without being expected to own the underlying engineering platform. \nTechnical Leadership & Mentoring\nProvide technical leadership on complex data science initiatives and help other data scientists work through difficult modeling and measurement problems. \nMentor data scientists through code reviews, modeling reviews, experiment design, pair work, and knowledge sharing. \nHelp establish practical standards for model development, experimentation, validation, documentation, explainability, and responsible AI. \nIntroduce new methods and tools when they can meaningfully improve how the team works. \nWork with Product, Engineering, and Data Engineering to continuously improve how data science moves from development into production. \nHelp shape the Data Science and AI roadmap by identifying opportunities and informing technical priorities. \nWhat Success Looks Like\nData science solutions are deployed and produce measurable improvements in student or business outcomes. \nModels are well validated, monitored in production, documented, and connected to real-world results. \nExperiments provide reliable evidence that helps Product and business teams make better decisions. \nComplex or ambiguous problems are turned into clear data science approaches and actionable recommendations. \nModels move effectively from development into production through strong partnership between Data Science and Engineering. \nOther data scientists grow technically through your mentorship and leadership. \nCross-functional partners see you as a trusted technical leader who can drive complex data science initiatives and communicate clearly about the decisions involved. \nHow Impact Will be Measured\nMeasurable improvements in retention, engagement, enrollment conversion, and other initiative-specific outcomes. \nQuality and reliability of models deployed to production. Quality and velocity of experimentation and the decisions those experiments enable. \nAbility to take complex data science initiatives from problem definition through production and outcome measurement. \nImprovements in team practices, technical quality, and the development of other data scientists. \nEffectiveness working across Product, Engineering, Customer Experience, Partnership, and other business teams. \nWhat You’ll Bring to the Team\nExperience That Matters Most\n8+ years of experience in applied data science, machine learning, or a related field, including technical leadership on complex projects.\nStrong hands-on experience building predictive models such as propensity, churn, retention, engagement, personalization, recommendation, or other behavioral models. \nStrong understanding of machine learning, statistics, feature engineering, and model evaluation. \nExperience designing and analyzing A/B tests used to make real product or business decisions. \nExperience with causal inference, uplift modeling, incrementality measurement, or related methods. \nStrong Python and SQL skills and experience with common ML libraries such as scikit-learn, XGBoost, TensorFlow, or PyTorch. \nExperience taking machine learning models into production and monitoring their performance after launch. \nExperience working with Data Engineering or Engineering teams to productionize data science solutions. \nExperience with modern data science and MLOps platforms such as Databricks and MLflow, or comparable technologies. \nExperience working with large, complex datasets from multiple sources. \nStrong judgment about data quality, model validation, monitoring, explainability, and responsible ML. \nExperience mentoring data scientists and providing technical direction on projects. \nAbility to turn complex technical work into clear recommendations for Product, Engineering, business partners, and senior leaders. \nBachelor's or Master's degree in computer science, statistics, econometrics, mathematics, engineering, or another quantitative discipline, or equivalent relevant experience. \nExperience That’s Great to Have\nPhD in a quantitative or technical discipline. \nExperience in higher education, edtech, consumer technology, personalization, or another complex behavioral domain. \nExperience building recommendation systems, next-best-action solutions, propensity models, or personalization systems. \nAdvanced experience with causal inference, uplift modeling, or treatment-effect estimation. \nExperience with speech analytics, NLP, conversational data, or other unstructured behavioral data. \nExperience integrating ML solutions with CRM, LMS, customer engagement, or marketing platforms. \nFamiliarity with responsible AI practices, including model transparency, fairness, and bias evaluation. \nExperience with AI-assisted data science and software development tools. \nRisepoint is an equal-opportunity employer and supports a diverse and inclusive 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