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Senior Software Developer (Quantitative Solutions)

CFRA Research
Remote, IND
Full-time
Senior
Remote
Discovered 6 days ago
PythonAWSApache SparkApache FlinkAWS GlueR
Free

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Key Responsibilities

  • Model Development: Lead the design and development of quantitative data engineering models, including algorithms, data pipelines, and data processing systems, to support business requirements.
  • Data Processing: Develop and maintain data processing pipelines to ingest, clean, transform, and aggregate large volumes of data from various sources, ensuring data quality and reliability.
  • Algorithm Development: Design and implement algorithms for data analysis, machine learning, and statistical modeling, using techniques such as regression analysis, clustering, and predictive modeling.
  • Performance Optimization: Identify and implement optimizations to improve the performance and efficiency of data processing and modeling algorithms, considering factors like scalability and resource utilization.
  • Data Visualization: Create visualizations of data and model outputs to communicate insights and findings to stakeholders.
  • Data Quality Assurance: Implement data quality checks and validation processes to ensure the accuracy, completeness, and consistency of data used in models and analyses.
  • Model Evaluation: Evaluate the performance of data engineering models using metrics and validation techniques, and iterate on models to improve their accuracy and effectiveness.
  • Collaboration: Collaborate with data scientists, analysts, and business stakeholders to understand requirements, develop models, and deliver insights that drive business decisions.
  • Documentation: Document the design, implementation, and evaluation of data
  • engineering models, including assumptions, methodologies, and results, to ensure reproducibility and transparency.
  • Continuous Learning: Stay updated with the latest trends, tools, and technologies in quantitative data engineering and data science, and continuously improve your skills and knowledge.

Skills, Knowledge and Expertise

  • 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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