Big Data Development Engineer Intern

About Us

Established in 2018, Bybit is one of the world’s leading cryptocurrency exchanges and digital financial platforms, serving over 80 million users across more than 200 countries and regions. Powered by world-class technology and a user-first mindset, Bybit delivers a seamless ecosystem across trading, payments, wealth management, custody, institutional services, and Web3 — connecting users to the future of digital finance.   Our core values define how we build. We listen, care and improve to create products and experiences that put users first. Backed by a global team of ambitious builders, problem-solvers, and innovators, we foster a high-performance and fast-moving environment where talent is empowered to drive real impact at the global scale. Supported by 24/7 multilingual customer service and a strong commitment to innovation, we are shaping the future of finance through technology, collaboration, and bold execution.   Today, Bybit is recognized as one of the most trusted and transparent platforms in the digital asset industry, continuing to expand its global presence while building the infrastructure for the next generation of financial services. Key Responsibilities:

1. Assess the Status Quo — Develop an end-to-end understanding of the campaign lifecycle and available data assets; identify all decision points currently reliant on manual judgment (opportunity identification, audience selection, budget estimation, campaign mechanics design, post-campaign review) and determine where intelligent automation yields the highest return.

2. What Campaign to Run — Mine campaign opportunities from user behavior, product and market signals, and historical campaign performance; build an AI Agent that outputs candidate campaign themes with clear supporting evidence, not just a ranked list.

3. Who to Target — Build or reuse user segmentation and response/propensity models; apply uplift modeling to direct budget toward truly incremental audiences who would be influenced, rather than users who would convert anyway.

4. How Much Budget — Build a cost-benefit model: projected participation, incremental trading volume / revenue, ROI simulation, budget allocation recommendations, and sensitivity and risk analysis (including abuse and reward-farming risks under incentive designs).

5. Feasibility Analysis & Plan Design — Translate model outputs into a reviewable proposal: objectives, mechanics, target audience, budget, expected KPIs, and risk control boundaries; align with PM, Growth, and business stakeholders.

6. MVP Delivery — Implement the end-to-end pipeline (data → model → recommendation → campaign configuration → A/B or holdout evaluation); run it on at least one real campaign or rigorous backtest; quantify improvement relative to the current manual decision-making baseline.