ABOUT US

Data Analysis Incorporated (DAI) is the controlling entity of the O’Neil family of businesses. DAI and its subsidiaries operate in diverse industries worldwide, including global equity markets, health care, financial services, digital news, and insurance. Our global footprint allows our teams to be responsive to customer needs in a timely and efficient manner. We are dedicated to using technology and innovation to bring change and growth to our businesses. We believe in a dynamic workplace, creating engaging, informative products and services that help our customers succeed. Integrity is an essential characteristic for our firms and our associates

SUMMARY

Conducts research on predictive modeling, optimization, automation, and latent data structures, leveraging advanced statistical and AI techniques. Develops methods and tools to improve decision-making in quantitative finance and other domains. Works across the firm’s operating units to apply data science, machine learning, and AI-driven approaches to a range of strategic and operational challenges.

QUALIFICATIONS & REQUIREMENTS

• Bachelor’s degree or higher in data science, math, engineering, or a related field
• 2-5 years prior experience working with project teams in a corporate environment
• Advanced skill with data science topics such as machine learning, data mining, statistics, and classification
• Advanced skill with SQL used to gather, analyze, and clean market data is required
• Advanced skill with Python libraries such as Pandas, NumPy, MlPy, MatPlotLib, and SciPy is required
• Working knowledge of tools such as R or MatLab is a plus
• Familiarity with financial statistics, common investment styles, and market cycles is a plus
• Familiarity with algorithmic trading & backtesting using Python (Zipline, PyFolio, AlphaLens, etc.) is a plus
• Personal interest in equity investments is a plus
• Highest levels of integrity and professional maturity
• Highly organized, analytical problem solver
• Collaborative teammate
• Clarity and precision in written and oral communication

• Conduct factor research to identify and analyze key drivers of investment performance.
• Develop predictive models to enhance investment decision-making, trade execution, and risk management.
• Research and implement electronic execution strategies to optimize market impact and trading efficiency.
• Apply AI and machine learning techniques to identify patterns, generate insights, and improve forecasting models.
• Design and refine robust, scalable methods for data analysis in finance and other business domains.
• Backtest and validate models and strategies to ensure robustness and practical viability.
• Collaborate with investment managers to integrate quantitative models into active strategies.
• Share research findings through reports, presentations, and internal knowledge-sharing sessions.
• Work on interdisciplinary projects within the firm’s operating units, applying AI and data science techniques to diverse challenges.
• Mentor junior team members and contribute to a culture of continuous learning and innovation.