Senior Software Developer-Quant - Full Time

Talent Zone Consultants

Bengaluru

On-site

INR 1,500,000 - 3,500,000

Full time

14 days+

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Job summary

A recruitment consultancy is seeking a Senior Software Developer in Bangalore. The role involves leading the development of data engineering models, overseeing data processing systems, and implementing algorithms for analysis. Candidates should have 4-6 years of experience and proficiency in programming languages like Python and tools such as AWS. This position emphasizes collaboration with cross-functional teams and requires strong problem-solving skills. Salary details are not disclosed.

Qualifications

4-6 years of experience in relevant fields.
Strong background in data engineering principles.
Proficiency in programming languages like Python, R, Java, or Scala.
Experience with cloud platforms such as AWS, Azure, or Google Cloud.
Ability to build data pipelines and machine learning models.

Responsibilities

Lead the design and development of quantitative data engineering models.
Develop and maintain data processing pipelines for large volumes of data.
Create visualizations of data and model outputs to communicate insights.
Optimize performance and resource utilization of data processing and modeling algorithms.
Create visualizations of data and model outputs to communicate insights.

Skills

Data Engineering

Quantitative Analysis

Programming Languages

Big Data Technologies

Data Visualization

Machine Learning Frameworks

Cloud Computing

Software Development

Problem-solving Skills

Communication and Collaboration

Domain Knowledge

Continuous Learning

Education

M.C.A

M.Sc

M.Tech

Tools

Apache Spark

Apache Flink

AWS Glue

Python

SQL

AWS

Tableau

Git

PyTorch

Job description

Job openings for Senior Software Developer in Bangalore

Senior Software Developer-Quant - Full Time

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.

Desired Skills and Experience

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.