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The Android / iOS Developer designs, develops, and maintains mobile applications for Android (using Java/Kotlin) and/or iOS (using Swift/Objective-C) platforms. They create user-friendly, high-performance apps, integrating APIs and third-party services, while ensuring compatibility, security, and compliance with platform guidelines. Responsibilities include coding, testing, debugging, and optimizing apps, collaborating with cross-functional teams, and managing app store submissions.

Job Title: Android / iOS Developer

Job Summary

The Android / iOS Developer is responsible for designing, developing, and maintaining high-quality mobile applications for Android and/or iOS platforms. This role involves collaborating with cross-functional teams to create user-friendly, performant, and scalable mobile apps that meet business requirements and deliver exceptional user experiences. The developer ensures apps are optimized for performance, security, and compatibility across devices.

Key Responsibilities

1. Application Development:Design and develop mobile applications for Android (using Java/Kotlin) and/or iOS (using Swift/Objective-C) based on project requirements.
   Implement user interfaces (UI) and user experiences (UX) following design specifications and guidelines (e.g., Material Design for Android, Human Interface Guidelines for iOS).
   Integrate RESTful APIs, third-party libraries, and backend services to enhance app functionality.
   
2. Code Quality and Testing:Write clean, maintainable, and efficient code following best practices and coding standards.
   Perform unit testing, integration testing, and debugging to ensure app reliability and performance.
   Use tools like Espresso (Android) or XCTest/XCUITest (iOS) for automated testing.
   
3. App Optimization and Maintenance:Optimize applications for performance, scalability, and battery efficiency across a range of devices and operating system versions.
   Monitor and resolve bugs, crashes, and performance issues reported through crash reporting tools (e.g., Firebase Crashlytics, Sentry).
   Update apps to support new OS versions, devices, and platform requirements.
   
4. Collaboration and Integration:Work closely with product managers, designers, backend developers, and QA teams to align app features with business goals.
   Collaborate on integrating mobile apps with cloud services, databases, and APIs (e.g., AWS, Firebase, GraphQL).
   Participate in agile development processes, including sprint planning, stand-ups, and code reviews.
   
5. App Store Management:Prepare and submit applications to the Google Play Store and/or Apple App Store, ensuring compliance with store guidelines.
   Manage app versioning, updates, and release cycles.
   Address user feedback and implement improvements based on app store reviews.
   
6. Security and Compliance:Implement security best practices, such as data encryption and secure API communication, to protect user data.
   Ensure compliance with privacy regulations (e.g., GDPR, CCPA) and platform-specific requirements.
   Maintain documentation for code, APIs, and app architecture.
   

Qualifications and Skills

• Education: Bachelor’s degree in Computer Science, Software Engineering, or a related field, or equivalent practical experience.
• Experience: 2-5 years of experience in mobile app development for Android and/or iOS platforms.
• Technical Skills:
  Android Development:Proficiency in Java and/or Kotlin.
  Experience with Android SDK, Android Studio, and Gradle.
  Knowledge of Android architecture components (e.g., LiveData, ViewModel, Room).
  Familiarity with Material Design guidelines and Jetpack libraries.
  
  iOS Development:Proficiency in Swift and/or Objective-C.
  Experience with Xcode, iOS SDK, and Interface Builder.
  Knowledge of iOS frameworks (e.g., UIKit, CoreData, SwiftUI).
  Familiarity with Apple Human Interface Guidelines.
  
  Shared Skills:Experience with RESTful APIs, JSON, and web services integration.
  Knowledge of version control systems (e.g., Git).
  Familiarity with mobile app testing frameworks and tools.
  Understanding of CI/CD pipelines for mobile app deployment (e.g., Jenkins, Fastlane).
  Experience with cross-platform frameworks (e.g., Flutter, React Native) is a plus.
  
• Soft Skills:Strong problem-solving and analytical skills.
  Excellent communication and collaboration abilities.
  Ability to work in a fast-paced environment and manage multiple projects.
  Attention to detail and a focus on delivering high-quality user experiences.
  
• Preferred Skills:Experience with cloud platforms (e.g., AWS, Firebase, Azure).
  Knowledge of mobile app security practices and performance optimization.
  Familiarity with Agile/Scrum methodologies.
  Certifications in Android (e.g., Google Associate Android Developer) or iOS development.

Data Scientist / Data Engineer, combining the key responsibilities, skills, and qualifications typically expected for these roles. While Data Scientist and Data Engineer roles are distinct, they often overlap in responsibilities, particularly in organizations where hybrid roles are common.

Job Title: Data Scientist / Data Engineer

Job Summary

The Data Scientist / Data Engineer is responsible for designing, developing, and implementing data-driven solutions to support business objectives. Data Scientists focus on analyzing complex datasets, building predictive models, and extracting actionable insights using statistical and machine learning techniques. Data Engineers focus on designing, building, and maintaining scalable data pipelines and infrastructure to ensure data availability, quality, and reliability. This role collaborates with cross-functional teams to transform raw data into valuable insights and enable data-driven decision-making.

Key Responsibilities

Data Scientist Responsibilities

1. Data Analysis and Modeling:Collect, clean, and analyze large datasets to identify trends, patterns, and insights.
   Develop and deploy machine learning models (e.g., regression, classification, clustering) to solve business problems.
   Perform statistical analysis and hypothesis testing to validate findings.
   
2. Insight Generation:Translate complex data findings into actionable business recommendations through visualizations, reports, and presentations.
   Collaborate with stakeholders to define business problems and translate them into analytical solutions.
   Conduct A/B testing and experimentation to optimize business processes or products.
   
3. Model Deployment and Monitoring:Deploy machine learning models into production environments, ensuring scalability and performance.
   Monitor model performance and retrain models as needed to maintain accuracy and relevance.
   

Data Engineer Responsibilities

1. Data Pipeline Development:Design, build, and maintain scalable and robust data pipelines to extract, transform, and load (ETL/ELT) data from various sources.
   Ensure data quality, consistency, and availability for downstream analytics and applications.
   Optimize data pipelines for performance, cost, and scalability.
   
2. Data Infrastructure Management:Develop and manage data storage solutions, including data warehouses (e.g., Snowflake, Redshift) and data lakes.
   Implement and maintain data integration tools and frameworks to support real-time and batch processing.
   Ensure data security, compliance, and governance in line with regulations (e.g., GDPR, CCPA).
   
3. Collaboration and Support:Work with Data Scientists, analysts, and business teams to understand data requirements and deliver solutions.
   Support the integration of data into business applications, dashboards, or machine learning models.
   Automate data workflows and processes to improve efficiency.
   

Shared Responsibilities

• Collaborate with cross-functional teams, including software engineers, product managers, and business analysts, to deliver data-driven solutions.
• Maintain documentation for data processes, models, and pipelines.
• Stay updated on emerging tools, technologies, and methodologies in data science and engineering.

Qualifications and Skills

Education

• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field. A Ph.D. is a plus for Data Scientist roles.

Experience

• Data Scientist: 2-5 years of experience in data analysis, machine learning, or statistical modeling.
• Data Engineer: 2-5 years of experience in data engineering, ETL pipeline development, or database management.
• Experience with cloud platforms (e.g., AWS, Azure, GCP) is highly desirable.

Technical Skills

• Data Scientist:Proficiency in programming languages such as Python or R for data analysis and modeling.
  Expertise in machine learning frameworks (e.g., TensorFlow, PyTorch, scikit-learn).
  Strong knowledge of statistical methods, data visualization tools (e.g., Tableau, Power BI), and SQL.
  Familiarity with big data technologies (e.g., Hadoop, Spark) is a plus.
  
• Data Engineer:Proficiency in programming languages such as Python, Java, or Scala.
  Expertise in SQL and database management systems (e.g., MySQL, PostgreSQL, MongoDB).
  Experience with ETL tools (e.g., Apache Airflow, Talend) and data warehousing solutions.
  Knowledge of cloud-based data platforms (e.g., AWS Redshift, Google BigQuery, Snowflake).
  Familiarity with containerization and orchestration tools (e.g., Docker, Kubernetes) is a plus.
  
• Shared Skills:Strong understanding of data structures, algorithms, and database design.
  Experience with version control systems (e.g., Git).
  Knowledge of data governance, security, and compliance standards.
  

Soft Skills

• Strong analytical and problem-solving skills.
• Excellent communication skills to present complex findings to non-technical stakeholders.
• Ability to work collaboratively in a team environment and manage multiple priorities.
• Attention to detail and a commitment to data accuracy and quality.

Preferred Skills

• Experience with real-time data processing or streaming technologies (e.g., Kafka, Flink).
• Familiarity with DevOps practices for data pipelines (e.g., CI/CD for data workflows).
• Knowledge of advanced analytics techniques, such as natural language processing (NLP) or deep learning, for Data Scientists.
• Certifications in cloud platforms (e.g., AWS Certified Data Analytics, Google Professional Data Engineer) or data science methodologies.

Work Environment

• Work is typically performed in an office or remote setting, with collaboration across teams.
• May involve working with large-scale datasets and computationally intensive tasks requiring access to cloud or high-performance computing resources.
• Flexible hours may be required to meet project deadlines or support global teams.