Full-Time

Data Scientist

Growth

Art of Problem Solving

Art of Problem Solving

501-1,000 employees

Online advanced math courses and materials

Compensation Overview

$137.8k - $169.6k/yr

No H1B Sponsorship

San Diego, CA, USA

Hybrid

US Top Secret Clearance Required

Category
Data & Analytics (1)
Required Skills
Scikit-learn
Redshift
Python
Git
BigQuery
SQL
A/B Testing
Pandas
Snowflake
Requirements
  • 2+ years of experience in data science, analytics, or a related quantitative role, within marketing and growth contexts (e.g., acquisition, lifecycle, campaigns), with demonstrated impact on decision making
  • Proven ability to operate as a strategic thought partner to marketing teams and leadership, building trust and influence across functions including creative, growth, and business stakeholders
  • Strong proficiency in Python for data analysis, statistical modeling, and machine learning (e.g., pandas or polars, scikit-learn, statsmodels)
  • Strong SQL and data warehouses (e.g., Redshift, Snowflake, BigQuery) skills. We use Redshift
  • Experience using tools and best practices for reproducible research (e.g., version-controlled analysis, environment management, documented workflows)
  • Strong foundation in experimentation and causal reasoning, including hypothesis testing, experiment design, and observational analysis
  • Experience defining and defending marketing metrics that drive alignment and decision-making
  • Excellent communication and the ability to translate complex analysis into clear, actionable guidance
  • Comfort owning work end-to-end, from raw data through executive-level recommendations
Responsibilities
  • Act as the primary data partner for the AoPS Marketing and Growth teams, supporting marketing strategy, campaign planning, and performance evaluation
  • Proactively identify opportunities, risks, and trends in customer acquisition data, surfacing insights without waiting for explicit requests
  • Frame ambiguous marketing and business problems, translating goals into testable hypotheses, success metrics, and analytic plans
  • Define, own, and evolve marketing and growth metrics, including funnel metrics, attribution models, customer acquisition costs, lifetime value, and channel efficiency, ensuring alignment across teams
  • Design and analyze experiments (e.g., A/B tests, creative tests, channel tests) to measure the impact of marketing campaigns and tactics
  • Design analytics data models and lightweight pipelines to support analytics and reporting as needed
  • Use SQL and Python to conduct analyses, develop models, and produce reproducible, decision-ready insights
  • Apply statistical and machine learning techniques where appropriate, while exercising strong judgment about rigor, speed, and tradeoffs
  • Communicate findings clearly, influencing marketing prioritization and budget allocation through data-backed recommendations, reports, and dashboards
  • Use best practices for marketing analytics, experimentation, and metric integrity, promoting consistent data quality and measurement standards across the organization
  • Completes other tasks and responsibilities, as assigned
Desired Qualifications
  • Experience with marketing attribution modeling and multi-touch attribution frameworks
  • Experience creating analytics data models and transformations. We use dbt
  • Experience using tools for product analytics. We use Mixpanel
  • Experience with BI and visualization tools (e.g., Lightdash, Tableau, Looker, PowerBI) and using them to enable self-service insights. We use Lightdash
  • Experience with customer segmentation, cohort analysis, and predictive modeling for customer lifetime value or churn
  • Experience using MLOps to ensure analyses and models are stable, repeatable, and maintainable
  • Familiarity with marketing platforms and data sources (e.g., Google Ads, Facebook Ads, email marketing tools, CRM systems). We use Active Campaign and Salesforce
  • Experience with distributed computing frameworks like PySpark or Dask
Art of Problem Solving

Art of Problem Solving

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Art of Problem Solving provides online math education for middle and high school students, focusing on advanced topics beyond the standard curriculum. It offers online courses, advanced math textbooks, and math games, plus computer science and programming classes. The products work by delivering structured lessons, practice problems, and interactive resources through its online learning platform and physical textbooks, often aligned with math competitions and STEM pathways. AoPS differentiates itself from competitors through a deep focus on problem-solving skills and Olympiad-style math content, targeted at motivated students and schools seeking high-quality math resources, with multiple channels including direct-to-consumer courses, textbooks, and institutional partnerships. The company's goal is to help students excel in mathematics and related fields, preparing them for competitive exams, STEM careers, and higher education through rigorous, high-level math education.

Company Size

501-1,000

Company Stage

N/A

Total Funding

N/A

Headquarters

San Diego, California

Founded

2003

Simplify Jobs

Simplify's Take

What believers are saying

  • Ben Kornell as CEO scales operations since June 1, 2025 transition.
  • Andrew Sutherland's CPO role accelerates language arts and science expansion.
  • New board members Ahmed Khaishgi and Dushyant Saraph add VC connections.

What critics are saying

  • Ben Kornell's non-edtech background causes subscriber churn in 6-12 months.
  • Sutherland's expansions dilute math expertise, alienating parents in 12-18 months.
  • Khan Academy's free AI tutor undercuts AoPS paid subscriptions immediately.

What makes Art of Problem Solving unique

  • AoPS specializes in Olympiad-level math for grades 5-12 competition prep.
  • AoPS Academy delivers hybrid physical classrooms with proprietary textbooks.
  • Over 500,000 annual students fuel AI-driven adaptive math personalization.

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Benefits

Health Insurance

Dental Insurance

Vision Insurance

401(k) Retirement Plan

401(k) Company Match

Paid Vacation

Relocation Assistance

Flexible Work Hours

Hybrid Work Options

Performance Bonus

Company News

VentureBeat
Apr 17th, 2025
Google’S Gemini 2.5 Flash Introduces ‘Thinking Budgets’ That Cut Ai Costs By 600% When Turned Down

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More. Google has launched Gemini 2.5 Flash, a major upgrade to its AI lineup that gives businesses and developers unprecedented control over how much “thinking” their AI performs. The new model, released today in preview through Google AI Studio and Vertex AI, represents a strategic effort to deliver improved reasoning capabilities while maintaining competitive pricing in the increasingly crowded AI market.The model introduces what Google calls a “thinking budget” — a mechanism that allows developers to specify how much computational power should be allocated to reasoning through complex problems before generating a response. This approach aims to address a fundamental tension in today’s AI marketplace: more sophisticated reasoning typically comes at the cost of higher latency and pricing.“We know cost and latency matter for a number of developer use cases, and so we want to offer developers the flexibility to adapt the amount of the thinking the model does, depending on their needs,” said Tulsee Doshi, Product Director for Gemini Models at Google DeepMind, in an exclusive interview with VentureBeat.This flexibility reveals Google’s pragmatic approach to AI deployment as the technology increasingly becomes embedded in business applications where cost predictability is essential. By allowing the thinking capability to be turned on or off, Google has created what it calls its “first fully hybrid reasoning model.”Pay only for the brainpower you need: Inside Google’s new AI pricing modelThe new pricing structure highlights the cost of reasoning in today’s AI systems

VentureBeat
Apr 16th, 2025
Openai Launches O3 And O4-Mini, Ai Models That ‘Think With Images’ And Use Tools Autonomously

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn MoreOpenAI launched two groundbreaking AI models today that can reason with images and use tools independently, representing what experts call a step change in artificial intelligence capabilities.The San Francisco-based company introduced o3 and o4-mini, the latest in its “o-series” of reasoning models, which it claims are its most intelligent and capable models to date. These systems can integrate images directly into their reasoning process, search the web, run code, analyze files, and even generate images within a single task flow.“There are some models that feel like a qualitative step into the future. GPT-4 was one of those. Today is also going to be one of those days,” said Greg Brockman, OpenAI’s president, during a press conference announcing the release. “These are the first models where top scientists tell us they produce legitimately good and useful novel ideas.”How OpenAI’s new models ‘think with images’ to transform visual problem-solvingThe most striking feature of these new models is their ability to “think with images” — not just see them, but manipulate and reason about them as part of their problem-solving process.“They don’t just see an image — they think with it,” OpenAI said in a statement sent to VentureBeat

PR Newswire
Mar 24th, 2025
Art Of Problem Solving Adds Ben Kornell As Ceo And Andrew Sutherland As Chief Product Officer; Current Ceo Richard Rusczyk Joins Board

SAN DIEGO, March 24, 2025 /PRNewswire/ -- Art of Problem Solving (AoPS), the leading provider of Advanced math, language arts, science, and computer science for grades 1–12, has hired Ben Kornell and Andrew Sutherland to lead the organization as CEO and Chief Product Officer, respectively. Founder and CEO Richard Rusczyk will transition to the Board on June 1, where he will join new AoPS Board members Ahmed Khaishgi and Dushyant Saraph

VentureBeat
Mar 5th, 2025
New Open-Source Math Model Light-R1-32B Surpasses Equivalent Deepseek Performance With Only $1000 In Training Costs

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn More. A team of researchers has introduced Light-R1-32B, a new open-source AI model optimized for solving advanced math problems, making it available on Hugging Face under a permissive Apache 2.0 license — free for enterprises and researchers to take, deploy, fine-tune or modify as they wish, even for commercial purposes. The 32-billion parameter (number of model settings) model surpasses the performance of similarly sized (and even larger) open source models such as DeepSeek-R1-Distill-Llama-70B and DeepSeek-R1-Distill-Qwen-32B on third-party benchmark the American Invitational Mathematics Examination (AIME), which contains 15 math problems designed for extremely advanced students and has an allotted time limit of 3 hours for human users.Developed by Liang Wen, Fenrui Xiao, Xin He, Yunke Cai, Qi An, Zhenyu Duan, Yimin Du, Junchen Liu, Lifu Tang, Xiaowei Lv, Haosheng Zou, Yongchao Deng, Shousheng Jia, and Xiangzheng Zhang, the model surpasses previous open-source alternatives on competitive math benchmarks.Incredibly, the researchers completed the model’s training in fewer than six hours on 12 Nvidia H800 GPUs at an estimated total cost of $1,000. This makes Light-R1-32B one of the most accessible and practical approaches for developing high-performing math-specialized AI models. However, it’s important to remember the model was trained on a variant of Alibaba’s open source Qwen 2.5-32B-Instruct, which itself is presumed to have had much higher upfront training costs.Alongside the model, the team has released its training datasets, training scripts, and evaluation tools, providing a transparent and accessible framework for building math-focused AI models.The arrival of Light-R1-32B follows other similar efforts from rivals such as Microsoft with its Orca-Math series.A new math king emergesLight-R1-32B is designed to tackle complex mathematical reasoning, particularly on the AIME (American Invitational Mathematics Examination) benchmarks

VentureBeat
Jan 22nd, 2025
Google Releases Free Gemini 2.0 Flash Thinking Model, Pressuring Openai’S Premium Strategy

Join our daily and weekly newsletters for the latest updates and exclusive content on industry-leading AI coverage. Learn MoreGoogle has quietly released a major update to its popular artificial intelligence model, Gemini, which now explains its reasoning process, sets new performance records in mathematical and scientific tasks, and offers a free alternative to OpenAI’s premium services.The new Gemini 2.0 Flash Thinking model, released Tuesday in the Google AI Studio under the experimental designation “Exp-01-21,” has achieved a 73.3% score on the American Invitational Mathematics Examination (AIME) and 74.2% on the GPQA Diamond science benchmark. These results show clear improvements over earlier AI models and demonstrate Google’s increasing strength in advanced reasoning.“We’ve been pioneering these types of planning systems for over a decade, starting with programs like AlphaGo, and it is exciting to see the powerful combination of these ideas with the most capable foundation models,” wrote Demis Hassabis, CEO of Google DeepMind, in a post on X.com (formerly Twitter).Our latest update to our Gemini 2.0 Flash Thinking model (available here: https://t.co/Rr9DvqbUdO) scores 73.3% on AIME (math) & 74.2% on GPQA Diamond (science) benchmarks. Thanks for all your feedback, this represents super fast progress from our first release just this past… pic.twitter.com/cM1gNwBoTO — Demis Hassabis (@demishassabis) January 21, 2025Gemini 2.0 Flash Thinking breaks records with million-token processingThe model’s most striking feature is its ability to process up to one million tokens of text — five times more than OpenAI’s o1 Pro model — while maintaining faster response times. This expanded context window allows the model to analyze multiple research papers or extensive datasets simultaneously, a capability that could transform how researchers and analysts work with large volumes of information.“As a first experiment, I took various religious and philosophical texts and asked Gemini 2.0 Flash Thinking to weave them together, extracting novel and unique insights,” Dan Mac, an AI researcher who tested the model, said in a post on X.com. “It processed 970,000 tokens in total