Data Scientist- ML

• Design, develop, and deploy machine learning models to solve real-world business problems.

• Develop production-quality machine learning pipelines and frameworks using Python and Java.

• Collaborate with data scientists, engineers, and business teams to define data requirements, model specifications, and system architecture.

• Preprocess and clean large datasets, perform feature engineering, and ensure data quality for ML applications.

• Implement, optimize, and deploy algorithms, ensuring scalability and performance in production systems.

• Work with cloud-based platforms (AWS, GCP, Azure) for deploying and scaling machine learning models.

• Stay current with the latest machine learning research and trends, and propose innovative solutions and techniques.

• Document processes, models, and code to ensure reproducibility, maintainability, and knowledge sharing.

Required Skills & Qualifications:

• 5-6 years of professional experience in software engineering, with at least 2-3 years of hands-on experience in machine learning and data science.

• Proficiency in Python for machine learning, data analysis, and algorithm development.

• Fair experience with Java, including the development of production-level applications and integration with ML models.

• Familiarity with data preprocessing techniques, including feature extraction, cleaning, normalization, and transformation.

• Strong experience with SQL and working with large datasets from databases and data lakes.

• Proficiency in cloud platforms (AWS, GCP, or Azure) for deploying machine learning models and managing infrastructure.

• Strong knowledge of version control systems like Git and experience with CI/CD pipelines for machine learning models.

• Strong problem-solving and debugging skills.

• Ability to work independently and in a collaborative team environment.

• Excellent communication skills for technical documentation and presenting solutions to stakeholders.

Desired Skills:

• Familiarity with big data technologies such as Spark, or Kafka.

• Experience with DevOps practices for automating ML workflows.

• Experience in deploying ML models using Docker, Kubernetes, or similar containerization technologies.