Python Job Vacancies in Bangalore

A data science job description for freshers focuses on collecting, cleaning, and analyzing structured/unstructured data to generate actionable insights. Key responsibilities include building predictive models using SQL, Python, or R, data visualization (Tableau/Power BI), and collaborating with teams to solve business problems. Top requirements are strong analytical, statistical, and communication skills

Role: Senior Software Engineer / Lead Software EngineerDate: 2026/April/06Experience: 4-6 yearsOpen Positions: 2Joining Timeline: Immediate (within 10-15 days)Job Description:Arnest is looking for experienced full-stack developers with strong AI/ML implementation expertise.The ideal candidate will have a proven track record of designing, developing, and deploying scalableapplications integrated with advanced analytics and machine learning models.You will play a key role in building intelligent AI solutions, working across the stack-from datapipelines and model development to frontend dashboards and system deployment.

We are looking for a talented and motivated Data Analyst to join our team. This full-time position is ideal for fresh graduates seeking to start their career in data analysis. The role is based in Bangalore, India, and offers the flexibility of working from home.As a Data Analyst, you will be responsible for collecting, analyzing, and interpreting data to help our team make informed decisions. You will engage in various tasks including:- **Data Collection:** Gathering data from different sources to ensure we have accurate information for analysis.- **Data Analysis:** Using statistical methods to analyze and interpret complex data sets, providing insights that drive strategies.- **Report Generation:** Creating reports and visualizations to present findings in a clear and understandable manner.- **Collaboration:** Working closely with other teams to understand their data needs and providing assistance where required.To succeed in this role, you should possess strong analytical skills and a keen eye for detail. You should be comfortable using data analysis tools and software and have a solid understanding of statistical methods. Good communication skills are essential, as you will need to explain your findings to team members and contribute to discussions about data-driven decisions.We expect you to be driven, eager to learn, and capable of working independently. Being organized and proactive in handling tasks will help you thrive in this remote work environment.

Key Responsibilities- Design, implement, and maintain DevOps technologies and processes, including analysis of current environments and selection of appropriate tools.- Develop and manage CI/CD pipelines for continuous integration, testing, and deployment across multiple applications.- Build automation solutions using Python and Shell scripting to streamline build, deployment, and operational tasks.- Implement infrastructure automation and configuration management using Ansible.- Integrate code quality, security, monitoring, and metrics tools into daily build and release pipelines.- Manage and support cloud-based production environments on AWS and/or Azure, including deployments and troubleshooting.- Work closely with development, QA, IT, and security teams to ensure adherence to DevOps standards and best practices.- Ensure compliance with defined organizational guidelines, processes, and delivery standards.- Coordinate activities of assigned team members and guide execution as per organizational objectives.________________________________________Required Skills & Experience- 4-6 years of overall IT/DevOps experience.- Strong hands-on experience in Python scripting and Python programming for automation, tooling, and integrations.- Solid understanding of DevOps principles and configuration management.- Proven experience in building CI/CD pipelines using Jenkins, Azure DevOps, and GitHub (YAML pipelines, vNext builds, scripted and freestyle Jenkins jobs).- Strong experience with Linux environments.- Hands-on experience with Cloud platforms (AWS and/or Azure), including managing production workloads.- Working experience with Kubernetes and container-based deployments.- Experience with Ansible for configuration management and infrastructure automation.- Hands-on administration of version control tools such as Git and Azure DevOps (ADO).- Experience with code quality and metrics tools such as SonarQube and TeamScale.- Strong experience in developing build workflows using Python, Shell, YAML, XAML, and TFS vNext.- Excellent analytical, debugging, and problem-solving skills.

Strong experience with automation tools (UiPath, Power Automate, Automation Anywhere) and scripting languages (Python, SQL, PowerShell). Knowledge of financial datasets, particularly ETFs, mutual funds, and market indices. Familiarity with cloud platforms (Azure, AWS, GCP) and data pipeline orchestration tools (Airflow, Prefect, DBT). Strong analytical and problem-solving skills with attention to detail. Excellent communication skills to collaborate with technical and non-technical stakeholders. Prior experience in financial services, investment research, or asset management. Understanding of data governance, compliance, and audit requirements in financial data environments. Certifications in RPA tools, cloud automation, or data engineering.

Key Responsibilities Model Development: Lead the design and development of quantitative data engineeringmodels, including algorithms, data pipelines, and data processing systems, to supportbusiness requirements. Data Processing: Develop and maintain data processing pipelines to ingest, clean,transform, and aggregate large volumes of data from various sources, ensuring dataquality and reliability. Algorithm Development: Design and implement algorithms for data analysis, machinelearning, and statistical modeling, using techniques such as regression analysis,clustering, and predictive modeling. Performance Optimization: Identify and implement optimizations to improve theperformance and efficiency of data processing and modeling algorithms, consideringfactors like scalability and resource utilization. Data Visualization: Create visualizations of data and model outputs to communicateinsights and findings to stakeholders. Data Quality Assurance: Implement data quality checks and validation processes toensure the accuracy, completeness, and consistency of data used in models andanalyses. Model Evaluation: Evaluate the performance of data engineering models using metricsand validation techniques, and iterate on models to improve their accuracy andeffectiveness. Collaboration: Collaborate with data scientists, analysts, and business stakeholders tounderstand requirements, develop models, and deliver insights that drive businessdecisions. Documentation: Document the design, implementation, and evaluation of dataengineering models, including assumptions, methodologies, and results, to ensurereproducibility and transparency. Continuous Learning: Stay updated with the latest trends, tools, and technologies inquantitative data engineering and data science, and continuously improve your skillsand knowledge.Desired Skills and Experience Data Engineering: Strong background in data engineering principles, including dataingestion, data processing, data transformation, and data storage, using tools andframeworks such as Apache Spark, Apache Flink, or AWS Glue. Quantitative Analysis: Proficiency in quantitative analysis techniques, includingstatistical modeling, machine learning, and data mining, with experience inimplementing algorithms for regression analysis, clustering, classification, and predictivemodeling. Programming Languages: Proficiency in programming languages commonly used fordata engineering and quantitative analysis, such as Python, R, Java, or Scala, as well asexperience with SQL for data querying and manipulation. Big Data Technologies: Familiarity with big data technologies and platforms, such asHadoop, Apache Kafka, Apache Hive, or AWS EMR, for processing and analyzing largevolumes of data. Data Visualization: Experience in data visualization techniques and tools, such asMatplotlib, Seaborn, or Tableau, for creating visualizations of data and model outputs tocommunicate insights effectively. Machine Learning Frameworks: Familiarity with machine learning frameworks andlibraries, such as PyTorch for implementing and deploying machine learning models. Cloud Computing: Experience with cloud computing platforms, such as AWS, Azure, orGoogle Cloud Platform, and proficiency in using cloud services for data engineering andmodel deployment. Software Development: Strong software development skills, including proficiency insoftware design patterns, version control systems (e.g., Git), and software testingframeworks, to develop robust and maintainable code. Problem-solving Skills: Excellent problem-solving skills, with the ability to analyzecomplex data engineering and quantitative analysis problems, identify solutions, andimplement them effectively. Communication and Collaboration: Strong communication and collaboration skills, withthe 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, ormarketing, depending on the industry, to understand the context and requirements ofdata engineering models in specific domains. Continuous Learning: A commitment to continuous learning and staying updated withthe latest trends, tools, and technologies in data engineering, quantitative analysis, andmachine learning.