Senior Software Developer, Quantitative Solutions
CFRA Research
full-time
Posted on:
Location Type: Remote
Location: Remote • 🇮🇳 India
Visit company websiteJob Level
Senior
Tech Stack
ApacheAWSAzureCloudGoogle Cloud PlatformHadoopJavaKafkaPythonPyTorchScalaSQLTableau
About the role
- 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.
Requirements
- 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
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
PythonRJavaScalaSQLHadoopApache KafkaApache HiveAWS EMRPyTorch
Soft skills
problem-solvingcommunicationcollaboration