Essential:

• 5-7 Years of Professional Experience: Demonstrated proficiency with 5-7 years of professional experience in software engineering, preferably emphasizing quantitative applications.

• Expert Knowledge of Python and Pandas: Mastery of Python and Pandas, coupled with proficiency in related scientific libraries like NumPy, SciPy, statsmodels, and scikit-learn.

• Experience in Mission-Critical Production Systems: Proven track record in developing mission-critical production systems, equipped with knowledge of best practices for testing, monitoring, and deployment.

• Proficiency on Linux Platforms and Understanding of Git: Strong command over Linux platforms and a thorough understanding of Git version control.

• Working Knowledge of Relevant Databases: Proficiency in at least one relevant database technology such as MS SQL, Postgres, or MongoDB.

• Experience with Large Data Sets: Demonstrated experience in handling large data sets, encompassing both structured and unstructured data.

Advantageous:

• Front-Office Quantitative Software Development Experience: Previous experience in quantitative software development within a front-office setting, including hedge funds, proprietary trading firms, or investment banks.

• Mentoring and Project Management: Experience in mentoring junior team members and managing projects.

• Web Application Development: Proficiency in building web applications using modern frameworks such as React.

• Knowledge of Distributed Computing Technologies: Familiarity with distributed computing technologies like Spark, Dask, Kubernetes, and Redis.

• Understanding of Modern Data Engineering Practices: Knowledge of modern data engineering practices, including data pipeline & ETL tools, distributed storage & processing, and data warehousing.

• Strong Understanding of Financial Markets: Thorough understanding of financial markets and instruments.

• Experience with Financial Market Data: Previous experience working with financial market data.

• Relevant Mathematical Knowledge: Understanding of relevant mathematical concepts such as statistics and time-series analysis.

Personal Attributes:

• Strong Academic Background: A strong academic record with a degree featuring high mathematical and computing content, such as Computer Science, Mathematics, Engineering, or Physics.

• Analytical Approach: Intellectually robust with a keenly analytic approach to problem-solving.

• Self-Organized: Ability to self-organize effectively, managing time across multiple projects and competing business demands and priorities.

• Value-Driven: Focused on delivering value to the business with relentless efforts to improve processes.

• Strong Interpersonal Skills: Ability to establish and maintain close working relationships with quantitative researchers, portfolio managers, traders, and senior business personnel.

• Confident Communicator: Able to articulate points concisely and positively engage with conflicting views.