Interested in this Data Scientist role at Success Academy Charter Schools?
Apply Now →Skills & Technologies
About This Role
Thanks for your interest in Success Academy! Running a large, fast\-growing, and high\-performing network of public charter schools takes a village \- families, children, teachers, staff and faculty, advocates, and supporters alike. We are growing fast in New York and expanding to Florida, and we would love to welcome you to our community! We work tirelessly every day to ensure children have access to a fun, rigorous, whole\-child education regardless of zip code or economic status. When you join SA, you play a part in giving every student who walks through our doors a fair shot at reaching his or her potential.
We are seeking a visionary and technically grounded Senior Data Scientist, Enterprise Data \& Analytics to lead the development, improvement, and long\-term sustainability of our data science models. In this role, you will bridge technical execution and strategic vision, translating complex educational and operational challenges into robust predictive architectures. Beyond building models, you will serve as a foundational mentor, championing technical best practices, elevating the team's capabilities, and fostering a culture of continuous learning and data science excellence. Key
Responsibilities:
- Model Development \& Innovation: Design, develop, and deploy end\-to\-end machine learning and statistical models to address critical institutional challenges (e.g., student performance trajectories, resource allocation, and operational efficiency).
- Sustainment \& Optimization: Audit, monitor, and continuously improve existing production models, ensuring their accuracy, reliability, and long\-term scalability.
- Technical Mentorship: Act as a primary mentor to data analysts and engineers, conducting code reviews, organizing internal knowledge\-sharing sessions, and disseminating current data science methodologies across the team.
- Data Strategy \& Querying: Architect efficient data extraction workflows and construct complex, scalable queries to manipulate large datasets for feature engineering and analysis.
- Collaboration: Work alongside data engineers and product owners to establish clean data pipelines and translate model outputs into actionable dashboards or software features.
What We Are Looking For (Requirements)
- Experience: 5\+ years of professional experience in data science, predictive modeling, or advanced analytics, with a proven track record of bringing machine learning models into production environments.
- Expert Python Proficiency: Deep expertise in Python and its core data science ecosystem (including pandas, NumPy, scikit\-learn, and related framework libraries) for building robust, clean, and reusable code.
- Advanced SQL Skills: Exceptional ability to write, optimize, and debug complex SQL queries against large relational databases or modern cloud data warehouses.
- Core Data Science Fundamentals: Strong command of statistical modeling, regression techniques, classification algorithms, and experimental design (A/B testing).
- Mentorship Mindset: Prior experience or a strong demonstrated desire to guide, mentor, and upskill junior team members while establishing technical standards.
Nice to Haves (Preferred Qualifications)
- Statistical Programming in R: Familiarity with R for exploratory data analysis, prototyping, or specific statistical packages.
- Cloud \& MLOps Tools: Exposure to cloud data infrastructure (such as AWS, GCP, or Snowflake) and basic ML orchestration/versioning tools (e.g., MLflow, Airflow, Git).
- Domain Context: Prior experience working within education, non\-profits, or public sector datasets, though a diverse background across other industries is highly valued.
Success Academy Charter Schools is an equal opportunity employer and actively encourages applications from people of all backgrounds. Compensation is competitive and commensurate with experience. Success Academy offers a full benefits program and opportunities for professional growth.
Privacy Policy:
By providing your phone number, you consent to receive text message updates from Success Academy regarding your application. Reply STOP to unsubscribe. View our Privacy Policy.
We are an equal opportunity employer and value diversity at our organization. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status. We actively seek applications from people of all backgrounds to strengthen our community and the perspectives needed to flourish in a multicultural world. Success Academy offers a full benefits program and opportunities for professional growth.
*Success Academy Charter Schools does not offer employment\-based immigration sponsorship.*
Salary Context
This $180K-$200K range is above the 75th percentile for Data Scientist roles in our dataset (median: $157K across 236 roles with salary data).
View full Data Scientist salary data →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 Success Academy Charter Schools, 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. Disclosed range: $180K to $200K.
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.
Success Academy Charter Schools AI Hiring
Success Academy Charter Schools has 2 open AI roles right now. They're hiring across Data Scientist, AI Product Manager. Based in New York, NY, US. Compensation range: $200K - $200K.
Location Context
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% above the national median.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.