Quantitative Software Developer

Quantitative Researchers are responsible for independently conducting quantitative financial research with a focus on statistical and predictive models. About Cubist Cubist Systematic Strategies, an affiliate of Point72, deploys systematic, computer-driven trading strategies across multiple liquid asset classes, including equities, futures and foreign exchange. The core of our effort is rigorous research into a wide range of market anomalies, fueled by our unparalleled access to a wide range of publicly available data sources. Role Quantitative Researchers are responsible for independently conducting quantitative financial research with a focus on statistical and predictive models. Successful researchers manage all aspects of the research process including methodology selection, data collection and analysis, testing, prototyping, backtesting, and performance monitoring. Desirable Candidates M.S., Ph.D., or Ph.D. candidates in finance, computer science, mathematics, physics, or other quantitative discipline. Programming in any of the following: C++, C#, Java, or Python. Strong analytical and quantitative skills. Keen interest in quantitative research and metrics driven decision making. Demonstrated ability to conduct independent research utilizing large data sets. Detail-oriented. Passion for spotting trends in data. Willingness to take ownership of his/her work, working both independently and within a small team. Ability to work under pressure. Prior experience developing, researching, or implementing quantitative models for equities is preferred, but not required. We will provide training for new researchers without finance backgrounds.

Role We are looking for an experienced Macro Quant Researcher to join our team in Taipei. Responsibilities Develop macro-focused systematic trading strategies in liquid secondary markets. Conduct research to identify data-driven signals and market inefficiencies. Collaborate with team members on research and development initiatives. Requirements B.S., M.S., or Ph.D. degree in economics, finance, computer science, physics, or other quantitative discipline. 2+ years of experience in quantitative research or systematic trading at a bank, hedge fund, or asset manager. Experience with systematic trading strategies for any secondary market product (e.g., Taiwan index futures, BTC, etc.) using tools beyond Excel or MultiCharts. Proficiency in Python or C++ and familiarity with database query languages (SQL or NoSQL). Demonstrable ability to conduct independent research utilizing large datasets. Detail-oriented, willingness to take ownership of his/her work, and ability to work both independently and within a small team. Commitment to the highest ethical standards.

Our vision is to transform how the world uses information to enrich life for all . Micron Technology is a world leader in innovating memory and storage solutions that accelerate the transformation of information into intelligence, inspiring the world to learn, communicate and advance faster than ever. We are seeking an experienced Data Science Manager to lead a team of data scientists and analysts focused on developing data-driven solutions that optimize cost, drive efficiency, and unlock value across procurement and manufacturing value engineering. In this role, you will own the delivery, and people leadership for a portfolio of cost-related analytics and AI initiatives, translating complex business challenges into measurable cost impact. You will also champion the adoption of cutting-edge AI, including Generative AI and agentic systems to accelerate how the team delivers cost solutions. Key Responsibilities Team Leadership & People Management Lead, mentor, and grow a team of data scientists. Set clear goals, conduct performance reviews, and support career development. Foster a collaborative, high-accountability culture of experimentation and continuous learning. Manage hiring, onboarding, and resource allocation across projects. Solution Development & Delivery Oversee end-to-end development of models and tools for cost forecasting, cost-leakage/anomaly detection, and scenario/what-if analysis. Drive analytics for manufacturing value engineering—identifying cost-down opportunities across design, materials, processes, and supplier options. Ensure solutions are production-ready, scalable, and integrated into business workflows and decision-making. Establish best practices for model development, va