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Binance Accelerator Program – Data Scientist (User Growth)

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Remote from
Singapore
Salary
Undisclosed
Department
Data Science & Analytics
Employment
Internship
Experience
Entry-Level, Junior
Published
Apply before
28 Nov 2026
Listing views
28
Application actions
3
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AI Summary

The role, at a glance.

This is a data science internship focused on user-growth algorithms for a global cryptocurrency platform. The intern will build user segmentation, recommendation, lifecycle, churn, conversion, and incentive-optimization models using behavioral data. Core work includes designing A/B tests, applying machine learning and causal-inference methods, and partnering with product, operations, and engineering teams. Candidates should be pursuing an advanced quantitative degree and be proficient in Python, SQL, and common machine-learning workflows. Experience with recommender systems, uplift modeling, multi-armed bandits, Spark, or Flink is advantageous.

Role DNA

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

Job Complexity

4/5
EasyHard

Pace & Pressure

5/5
RelaxedFast-paced

Autonomy Level

4/5
GuidedFull ownership

Communication Load

4/5
IndependentCollaborative
AI insightThe role requires practical machine-learning modeling, experimentation, and rigorous analysis of large-scale user behavior data. Its fast-paced growth focus and cross-functional delivery expectations make it demanding even for an internship.

Salary analysis

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

Estimated job median (per month)Market rate
$2,000
US market range (per month)$1.50k–$3k
AI insightThe monthly pay period is inferred from the job context; it was not explicitly stated by the employer. No employer pay amount is disclosed. Estimated monthly internship compensation is USD 1,500–3,000, with a midpoint of USD 2,000; this is an AI estimate for a data-science internship in Singapore, not verified employer compensation.

Core skills

Skills and capabilities most closely associated with this opportunity.

PythonSQLMachine LearningA/B TestingCausal InferenceUser SegmentationRecommender SystemsPredictive ModelingChurn PredictionLTV ModelingNumPyPandasScikit-learnSparkFlink
Sample interview questions
How would you design an A/B test to evaluate a new user incentive strategy?

I would define the target population, randomize eligible users into control and treatment groups, and set a primary metric such as incremental conversion or retained value. I would also establish guardrail metrics, calculate the required sample size and test duration, check randomization balance, and evaluate statistical significance alongside practical business impact.

How would you approach building a churn-prediction model?

I would first define churn precisely and choose an observation window and prediction horizon. I would create behavioral, transaction, engagement, and lifecycle features, use time-aware train-validation splits to avoid leakage, and compare interpretable baselines such as logistic regression with tree-based models such as GBDT. I would evaluate ranking and calibration metrics, then connect model scores to targeted retention actions.

What is the difference between correlation and causal impact in a growth analysis?

Correlation identifies an association but does not show whether an intervention caused an outcome. Causal impact requires a valid counterfactual, commonly established through randomized experiments or carefully designed quasi-experimental methods. For growth decisions, causal estimates are essential because targeting users who were already likely to convert can otherwise overstate impact.

How would you use SQL and Python together to investigate a drop in conversion?

I would use SQL to extract a clean event-level funnel segmented by cohort, channel, device, geography, and user lifecycle stage. In Python, I would validate data quality, visualize trends, quantify changes across segments, and test hypotheses about where the funnel shifted. I would then communicate the most likely drivers and recommend follow-up experiments or product investigations.

When might uplift modeling be more useful than a standard propensity model?

A propensity model predicts which users are likely to take an action, while uplift modeling estimates which users are likely to change behavior because of a treatment. Uplift is more useful for targeting incentives or campaigns because it can prioritize persuadable users and avoid spending on users who would convert anyway or may respond negatively.

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.

Responsibilities:

  • Participate in the design and development of algorithm strategies for user growth, including but not limited to user segmentation, intelligent recommendation, and incentive strategy optimization.

  • Build user lifecycle models (LTV prediction, churn warning, conversion prediction, etc.) based on user behavior data to drive precision operations and decision-making.

  • Design and implement A/B testing experiments to evaluate the effectiveness of growth strategies and drive continuous iteration and optimization.

  • Explore user characteristics and behavioral patterns using machine learning and causal inference methods to improve user retention and conversion.

  • Collaborate closely with product, operations, and engineering teams to translate algorithmic capabilities into measurable business growth outcomes.

Requirements:

  • Currently pursuing a Master’s or Ph.D. degree in Computer Science, Statistics, Mathematics, Artificial Intelligence, or a related field. Available to intern for at least 4 days per week, with a minimum internship duration of 3 months.

  • Solid foundation in machine learning, familiar with common models (e.g., LR, GBDT, DNN) and their applicable scenarios.

  • Proficient in Python, with hands-on experience using NumPy, Pandas, and Scikit-learn for data analysis and modeling.

  • Strong SQL skills, able to independently extract and analyze data from databases.

  • Familiarity with at least one of the following areas is preferred: recommender systems, causal inference, reinforcement learning, or operations research/optimization.

  • Prior internship or project experience in user growth, computational advertising, search/recommendation systems is a plus.

  • Strong communication skills and self-motivation, with the ability to learn quickly and solve problems independently.

Nice-to-Haves

  • Experience in data science competitions (e.g., Kaggle, Tianchi) with notable achievements.

  • Familiarity with growth-specific methods such as Uplift Modeling and Multi-Armed Bandit.

  • Experience with large-scale data processing frameworks (e.g., Spark, Flink).

  • Publications in machine learning, data mining, or related fields.

Why Binance

• Shape the future with the world’s leading blockchain ecosystem

• Collaborate with world-class talent in a user-centric global organization with a flat structure

• Tackle unique, fast-paced projects with autonomy in an innovative environment

• Thrive in a results-driven workplace with opportunities for career growth and continuous learning

• Competitive salary and company benefits

• Work-from-home arrangement (the arrangement may vary depending on the work nature of the business 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 confirm that you have read and agree to 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. 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.

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.

Apply now >

This job listing has been manually reviewed by the Jobicy Trust & Safety Team for compliance with our posting guidelines, including verification of the company's legitimacy, accuracy of job details, clarity of remote work policy, and absence of misleading or fraudulent content.

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