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About This Role
Risepoint is an education technology company that provides world\-class support and trusted expertise to more than 100 universities and colleges. We primarily work with regional universities, helping them develop and grow their high\-ROI, workforce\-focused online degree programs in critical areas such as nursing, teaching, business, and public service. Risepoint is dedicated to increasing access to affordable education so that more students, especially working adults, can improve their careers and meet employer and community needs.
Join Our Mission
Risepoint is an education technology company that provides world\-class support and trusted expertise to more than 100 universities and colleges. We primarily work with regional universities, helping them develop and grow their high\-ROI, workforce\-focused online degree programs in critical areas such as nursing, teaching, business, and public service. Risepoint is dedicated to increasing access to affordable education so that more students, especially working adults, can improve their careers and meet employer and community needs.
The Impact You Will Make
In this role, you will own the intelligence layer that powers how Risepoint engages with students at every stage of their journey. You will lead end\-to\-end initiatives—from scoping and design through cross\-functional implementation and measurable outcomes—that directly shape retention, engagement, and enrollment results for thousands of students across more than 100 university partners.
You will be accountable for delivering the “Next Best Experience” platform: the predictive engine that turns raw behavioral signals into personalized, timely outreach. Your decisions will determine who gets reached, when, and how—translating data science into student outcomes that help working adults succeed in programs that change their lives.
You will bring our mission to life by leading initiatives that make the student journey smarter and more human at the same time. Every initiative you own—from scoping a churn\-risk model through deploying it into production and measuring its downstream impact—translates directly into a real person getting the support they need before they fall through the cracks. By driving cross\-functional alignment and accountability across Product, Engineering, and CX teams, you will help Risepoint’s university partners serve more students more effectively.
How You Will Bring Our Mission to Life
What You Will Do
Initiative Leadership \& Cross\-Functional Ownership
- Lead AI/ML initiatives end\-to\-end—scoping, designing, managing implementation, and driving outcomes—coordinating across Product, Engineering, CX, and university partner teams.
- Own accountability for delivering measurable business outcomes from each initiative: retention lift, engagement improvement, enrollment conversion, and pipeline efficiency.
- Drive alignment and decision\-making across teams at each stage of an initiative’s lifecycle, from defining success metrics through post\-deployment iteration.
- Identify and scope net\-new AI/ML opportunities that deliver impact for students, university partners, and Risepoint’s business, and advocate for prioritization with leadership.
- Manage relationships with key vendors and software providers as a workstream leader, ensuring delivery commitments are met.
Model Development \& Production Delivery
- Build and deploy predictive models—including churn risk, engagement propensity, and success likelihood—that power proactive student outreach and are monitored continuously in production.
- Lead the design and implementation of “next best action” logic in close partnership with Product and CX, from logic design through production deployment.
- Prototype, test, and productionize models using MLOps frameworks (Databricks, MLFlow, dbt, Dagster), owning the full model lifecycle.
- Partner with data engineers to ensure clean, reliable pipelines and feature stores that support model development and production deployment at scale.
- Work with speech analytics and structured CRM/LMS data to derive behavioral insights across the student lifecycle.
Experimentation \& Performance Accountability
- Design and lead A/B testing programs to measure model\-driven impact on retention, engagement, and satisfaction, owning the decision to ship, iterate, or stop.
- Establish feedback loops and real\-world performance monitoring frameworks that enable continuous model improvement.
- Translate complex technical findings into clear, executive\-ready narratives that drive cross\-functional alignment and action.
Team Leadership \& Standards
- Mentor teammates and raise the team’s technical bar through code reviews, pair work, and knowledge\-sharing.
- Model ownership, adaptability, and initiative leadership in a fast\-changing environment; set the standard for what it means to own a workstream end\-to\-end.
What Success Looks Like
- Predictive models are deployed, monitored, and demonstrably improving student outcomes (e.g., reduced churn, higher engagement rates)—and you can point to specific initiative decisions you made that drove those results.
- Cross\-functional partners in Product, Engineering, and CX describe you as a leader who owns outcomes, not just analysis—who drives alignment, manages implementation, and delivers results.
- Experiment programs are well\-designed, velocity is high, and a clear percentage of tests yield statistically significant outcomes that inform production decisions.
- The data foundation is materially stronger because of your workstream ownership: pipelines are cleaner, features are better documented, and the team ships faster.
- You are actively raising the team’s technical standard and mentoring teammates toward greater ownership and impact.
How Impact Will be Measured
- Business outcomes tied to model\-driven initiatives: retention rates, re\-engagement rates, enrollment completion, and conversion lift.
- Initiative delivery: on\-time scoping, cross\-functional execution, and outcome realization against defined success metrics.
- Model performance metrics: accuracy, precision, recall, and AUC across deployed models; degradation alerts and retraining cadence.
- Experiment velocity and signal rate: number of A/B tests shipped per quarter and percentage yielding statistically significant, actionable results.
- Qualitative feedback from Product, Engineering, and CX partners on initiative ownership, communication quality, and cross\-functional effectiveness.
What You’ll Bring to the Team
Experience That Matters Most
- A proven track record of delivering measurable consumer and business impact through AI/ML initiatives—scoping, managing implementation, and owning outcomes end\-to\-end.
- Experience as a workstream leader: designing, managing, and delivering AI/ML projects in a cross\-functional environment.
- 5–8\+ years in applied machine learning or data science, ideally in education, consumer tech, personalization, or a complex behavioral domain.
- Strong background in predictive analytics, recommendation systems, and experimentation (A/B testing, causal inference, uplift modeling).
- Deep expertise in Python and SQL; proficiency with ML libraries (scikit\-learn, XGBoost, TensorFlow, or PyTorch).
- Experience with Databricks, MLFlow, dbt, and Dagster—or demonstrated ability to ramp quickly on a modern MLOps stack.
- Comfort working with complex, multi\-source datasets (CRM, LMS, communication logs, speech analytics).
- Excellent communicator across technical and non\-technical audiences, including executives; you make the science accessible without losing rigor.
- Bachelor’s or Master’s degree in a technical discipline (computer science, statistics, econometrics, mathematics, or engineering).
Experience That’s Great to Have
- PhD in a technical discipline (not required, but valued).
- Experience in higher education, edtech, or student success platforms.
- Familiarity with human\-in\-the\-loop AI systems and responsible ML practices (bias mitigation, model transparency, fairness metrics).
- Prior work building or operationalizing next best action or propensity\-to\-engage models at scale.
*Risepoint is an equal\-opportunity employer and supports a diverse and inclusive workforce.*
Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Risepoint, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $198,000 based on 808 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Risepoint AI Hiring
Risepoint has 2 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer. Based in Remote, US.
Remote Work Context
Remote AI roles pay a median of $170,000 across 1,926 positions. About 15% of all AI roles offer remote work.
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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