Senior Regulatory Affairs Specialist in Singapore

Ready to do the most impactful work of your career? At Coinbase , we are uncompromising on our mission to increase economic freedom. The bar is high, the environment is intense, and we like it that way. This isn't a place for complacency, it’s a place to be pushed past your perceived limits. If you're ready to build the future of finance alongside people who refuse to settle for "good enough," you belong here. Coinbase is a remote-first, but not remote-only company. Expect to get together quarterly for intense in-person working sessions called “surges.” learn more about working at Coinbase . Data Science sits at the heart of how Coinbase builds products and makes decisions. We partner with Product, Engineering, and Design to shape roadmaps, understand our users, and move the company's bottom line through rigorous experimentation, analytics, and advanced modeling. As a Data Scientist on the Consumer team, you'll improve the customer experience across Coinbase's consumer products — including spot and advanced trading, yield, Coinbase One, international expansion, and new launches such as prediction markets and equities trading. You'll define key metrics, build measurement and causal frameworks, and translate insights directly into product strategy and shipped improvements. What you'll do: Drive analysis and deep dives on ambiguous business problems, delivering insights and recommendations that guide team decision-making Own a broad scope of data and metrics — from core logging to polished data visualizations Lead code reviews, contribute SQL and Python expertise, and maintain reliable ETL pipelines Uphold a high bar for statistical rigor, ensuring experimentation and causal analyses build genuine confidence with stakeholders Required Skills and Experience: BA/BS in a quantitative field (Math, Stats, Physics, CS, or similar) with 5+ years of relevant experience, or a PhD with 3+ years of relevant experience Proven track record of delivering impa

Senior Engineer / Engineer - HIG HBM PSE — Fab 10A, Singapore. Apply via Workday.

About BlockTech BlockTech is an algorithmic trading firm operating at the frontier of global crypto derivatives and spot markets. We trade 24/7 across some of the fastest-moving, most data-rich venues in finance. Crypto remains one of the few markets where a researcher can still meaningfully move the edge: abundant data, novel microstructure, and the shortest possible loop between a research idea and live PnL. We're looking for an experienced Quantitative Researcher to take ownership of that edge and push it further. The role This is a senior, hands-on research seat on our trading floor. You'll own a research agenda end-to-end from hypothesis, dataset construction, feature engineering, model training, backtesting, live deployment, monitoring, and iteration. You'll be trusted to set its direction. You'll work shoulder-to-shoulder with fellow researchers, traders and analysts, shape how we price and trade, and help raise the bar for research across the floor, including mentoring less experienced researchers and influencing the tools and standards the team relies on. What you'll do Own price-prediction, signal, execution, and anomaly-detection models across crypto derivatives and spot markets from idea to live PnL using state-of-the-art ML Shape our research, backtesting, and trading infrastructure together with engineers, so good ideas reach production quickly and safely Own models in production: monitor live performance, diagnose decay, and iterate on what you ship Set research direction alongside traders, deciding which trades are worth making and why Raise the research bar by mentoring colleagues, reviewing work, and setting standards for rigour What we're looking for 4+ years of hands-on quantitative research and/or applied ML experience, with a track record of models you've taken into production trading live A strong academic foundation in a quantitative discipline (mathematics, physics, statistics, computer science, ML/AI, or similar) Fluency in Python and the m

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