Senior Software Developer - Quantitative Solutions

January 22

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Logo of CFRA Research

CFRA Research

CFRA Research is a global leader in delivering independent and unbiased investment research and financial intelligence solutions to clients worldwide. The company specializes in providing a comprehensive range of research services, including fundamental equity research, forensic accounting research, and public policy and legal research. Catering to a diverse clientele, CFRA serves wealth management firms, institutional investors, financial advisors, and self-directed individual investors by equipping them with the necessary data and analytics to navigate market cycles and make informed investment decisions. Notably, CFRA's award-winning technology and content delivery methods enable clients to integrate its insights seamlessly into their operations, ensuring flexible and cost-effective solutions across various platforms, such as web-based or API integrations. Their expertise spans consumer, energy, financials, healthcare, and technology sectors, among others, helping clients enhance portfolio performance and mitigate risks through high-quality research and data analytics.

Forensic Accounting Research, Analytics & Advisory • Risk Mitigation • Idea Generation • Investment Research

51 - 200 employees

Founded 1994

💸 Finance

💳 Fintech

🏢 Enterprise

💰 Private Equity Round on 2023-01

Description

• The Senior Software Developer will be responsible for development of CFRA’s next generation of quantitative solutions using a modern cloud-native technology stack with Python on AWS cloud infrastructure. • This is a rare opportunity to make a big impact on both the team and the organization by being part of the initial design and development of a new customer-facing application framework that will serve as the foundation for all future development at CFRA. • The ideal candidate has a passion for solving business problems with technology and can effectively communicate business and technical needs to stakeholders. • We are looking for candidates that value collaboration with colleagues and having an immediate, tangible impact for a leading global independent financial insights and data company. • The team uses a contemporary stack in the AWS cloud to design, build, and maintain robust data delivery pipelines via APIs and Feeds.

Requirements

• Data Engineering: Strong background in data engineering principles, including data ingestion, data processing, data transformation, and data storage, using tools and frameworks such as Apache Spark, Apache Flink, or AWS Glue. • Quantitative Analysis: Proficiency in quantitative analysis techniques, including statistical modeling, machine learning, and data mining, with experience in implementing algorithms for regression analysis, clustering, classification, and predictive modeling. • Programming Languages: Proficiency in programming languages commonly used for data engineering and quantitative analysis, such as Python, R, Java, or Scala, as well as experience with SQL for data querying and manipulation. • Big Data Technologies: Familiarity with big data technologies and platforms, such as Hadoop, Apache Kafka, Apache Hive, or AWS EMR, for processing and analyzing large volumes of data. • Data Visualization: Experience in data visualization techniques and tools, such as Matplotlib, Seaborn, or Tableau, for creating visualizations of data and model outputs to communicate insights effectively. • Machine Learning Frameworks: Familiarity with machine learning frameworks and libraries, such as PyTorch for implementing and deploying machine learning models. • Cloud Computing: Experience with cloud computing platforms, such as AWS, Azure, or Google Cloud Platform, and proficiency in using cloud services for data engineering and model deployment. • Software Development: Strong software development skills, including proficiency in software design patterns, version control systems (e.g., Git), and software testing frameworks, to develop robust and maintainable code. • Problem-solving Skills: Excellent problem-solving skills, with the ability to analyze complex data engineering and quantitative analysis problems, identify solutions, and implement them effectively. • Communication and Collaboration: Strong communication and collaboration skills, with the ability to work effectively with cross-functional teams, including data scientists, analysts, and business stakeholders, to understand requirements and deliver solutions. • Domain Knowledge: Domain knowledge in areas such as finance, healthcare, or marketing, depending on the industry, to understand the context and requirements of data engineering models in specific domains. • Continuous Learning: A commitment to continuous learning and staying updated with the latest trends, tools, and technologies in data engineering, quantitative analysis, and machine learning.

Benefits

• 21 days of Annual Vacation • 8 sick days • 6 casual days • 1 paid Volunteer Day • Medical, Accidental & Term Life Insurance • Telehealth, OPD • Competitive pay • Annual Performance Bonus

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