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Binance Accelerator Program – Data Scientist (Recommendation/Square Community)

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Remote from
Singapore
Salary
Undisclosed
Employment
Full Time
Experience
Open level
Published
Apply before
28 Nov 2026
Listing views
23
Application actions
2
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AI Summary

The role, at a glance.

Binance is seeking an early-career Data Scientist to improve recommendations and community intelligence for Binance Square. The role analyzes user behavior, content and creator quality, and feed engagement to guide ranking, personalization, cold-start, and traffic-allocation strategies. The hire will run A/B tests, monitor metrics, investigate poor recommendation cases, and collaborate with product, engineering, algorithm, and business teams. It also offers exposure to LLM and multimodal applications for content understanding, event detection, and personalized distribution. This is a full-time early-career opportunity suited to a current student with strong SQL, Python, statistics, and machine-learning foundations.

Role DNA

A quick view of the complexity, pace, ownership and collaboration implied by the job description.

Job Complexity

4/5
EasyHard

Pace & Pressure

4/5
RelaxedFast-paced

Autonomy Level

3/5
GuidedFull ownership

Communication Load

4/5
IndependentCollaborative
AI insightThe work requires practical statistical reasoning, experimentation, recommendation-system concepts, and the ability to turn behavioral data into product improvements. Although aimed at early-career talent, the role operates in a fast-moving crypto product environment and involves cross-functional technical collaboration.

Salary analysis

Estimated compensation compared with the broader US market for similar roles.

Estimated job median (per year)Market rate
$50,000
US market range (per year)$40k–$65k
AI insightThe yearly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer salary range is disclosed. Estimated annual compensation for an early-career data scientist in this type of role is shown in USD for comparison; the yearly cadence is inferred from the full-time professional role and no employer compensation amount is implied.

Core skills

Skills and capabilities most closely associated with this opportunity.

Sample interview questions
How would you evaluate whether a new feed-ranking model improves the Binance Square user experience?

I would define primary metrics such as qualified engagement, dwell time, return rate, and creator/content diversity, along with guardrail metrics for hides, reports, latency, and concentration. I would run a randomized A/B test, validate experiment integrity, assess statistical significance and practical effect size, and segment results by user cohort and content type.

How would you approach a cold-start problem for a new user in a recommendation system?

I would combine contextual signals such as locale, onboarding interests, referral source, and current market trends with broadly high-quality and diverse content. As the user interacts with the feed, I would quickly update their profile using clicks, dwell time, follows, saves, and negative feedback while ensuring sufficient exploration.

What is the difference between correlation and causation in product analytics?

Correlation shows that two variables move together, while causation means that changing one variable produces a change in the other. For product decisions, I would prefer randomized experiments where possible; when experimentation is not feasible, I would use careful observational methods and explicitly account for confounders.

How would you investigate a recommendation bad case where users receive irrelevant content?

I would first reproduce and categorize the case by user cohort, content type, retrieval source, ranking stage, and recent behavior. Then I would inspect feature values, logging completeness, candidate diversity, model scores, and feedback signals to identify whether the issue comes from data quality, recall, ranking, or policy constraints before proposing and testing a fix.

How could LLMs or multimodal models support content recommendations in a Web3 community?

They can generate richer representations of text, images, and videos; improve topic tagging; identify emerging events; and help detect duplicate or low-quality content. I would validate their outputs against human-labelled data, monitor cost and latency, and use the resulting features within a controlled recommendation experiment.

This analysis is generated from the job description. Salary estimates, role characteristics and sample answers are guidance, not employer-provided facts.
Opportunity details

About this role.

Binance is a leading global blockchain ecosystem behind the world’s largest cryptocurrency exchange by trading volume and registered users. We are trusted by 300+ million people in 100+ countries for our industry-leading security, user fund transparency, trading engine speed, deep liquidity, and an unmatched portfolio of digital-asset products. Binance offerings range from trading and finance to education, research, payments, institutional services, and more. Our mission is to increase the freedom of money and financial access for people around the world.

About the Role

We are looking for a Data Scientist early careers talent to join the Binance Square team and support the development of next-generation recommendation and community intelligence systems.

Binance Square is evolving from a content feed into an intelligent Web3 financial community, helping users discover market trends, trading ideas, hot topics, and high-quality creators. In this role, you will work closely with data scientists, algorithm engineers, product managers, and business teams to improve content distribution, user engagement, and community growth through data-driven recommendation strategies.

Responsibilities

Analyze user behavior, content quality, creator performance, and feed consumption patterns to identify opportunities for recommendation improvement.
Support the optimization of key recommendation scenarios, including Feed, Hot Tab, Topic, News, Trading Analysis, and creator distribution.
Build and refine data metrics for content quality, user interest profiling, creator quality, community health, and trading-related content.
Participate in recommendation strategy design, including recall, ranking, cold-start, diversity, personalization, and traffic allocation.
Conduct A/B testing, metric monitoring, bad-case analysis, and experiment deep dives to evaluate algorithm and product impact.
Explore the application of LLMs and multimodal models in content understanding, topic tagging, hot event detection, and personalized distribution.
Work with engineering teams to improve data pipelines, feature logging, experiment tracking, and recommendation system efficiency.

Requirements

Currently pursuing a Bachelor’s, Master’s, or PhD degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field.
Strong analytical skills and solid understanding of statistics, machine learning, and data mining.
Proficient in SQL and at least one programming language such as Python.
Familiar with recommendation systems, ranking models, user profiling, A/B testing, or causal analysis.
Good business sense and ability to translate data insights into product or algorithm improvements.
Strong communication skills and ability to work cross-functionally with product, algorithm, engineering, and operations teams.
Interest in Web3, crypto, financial markets, online communities, or content recommendation is a plus.

Why Binance

  • Shape the future of finance
  • Collaborate with world-class talent in a user-centric global organization with a flat structure
  • Tackle challenging projects in a fast-paced and innovative enviornment with autonomy
  • Thrive in a results-driven workplace with opportunities for career growth and continuous learning
  • Competitive salary and company benefits
  • Remote-first, work-from-home arrangement (may vary depending on the nature of the role and team)

Binance is committed to being an equal opportunity employer. We believe that having a diverse workforce is fundamental to our success.

By submitting a job application, you acknowledge that you have read our Candidate Privacy Notice.

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.

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