Insurance Information Bureau - Machine Learning Engineer (3-5 yrs)

Job Description : Machine Learning Engineer (3-5 Years of Experience)

About Organization : Insurance Information Bureau of India is an organization set up by IRDAI as a data repository of the insurance industry data. It handles huge data and provide services to the stake holders through web services and applications.

It is totally Technology driven. It analyses the data and give various standard and customized insights to the Insurance Industry & including Industry trends of key metrics and helps the IRDAI with necessary out comes that are crucial in pricing the products. In order to perform all the above listed activities, the Bureau engages the Data Scientists, Actuarial Analysts, Business analyst, SME/Domain experts and IT technical people.

Position Overview :

We are seeking an experienced Machine Learning Engineer with 3 to 5 years of hands-on experience in designing, developing, and deploying machine learning models and systems.

The ideal candidate will work closely with data scientists, software engineers, and product teams to create solutions that drive business value. You will be responsible for building scalable and efficient machine learning pipelines, optimizing model performance, and integrating models into production

Key Responsibilities :

- Model Development & Training : Develop and train machine learning models, including supervised, unsupervised, and deep learning algorithms, to solve business problems.

- Data Preparation : Collaborate with data engineers to clean, preprocess, and transform raw data into usable formats for model training and evaluation.

- Model Deployment & Monitoring : Deploy machine learning models into production environments, ensuring seamless integration with existing systems and monitoring model performance.

- Feature Engineering : Create and test new features to improve model performance, and optimize feature selection to reduce model complexity.

- Algorithm Optimization : Research and implement state-of-the-art algorithms to improve model accuracy, efficiency, and scalability.

- Collaborative Development : Work closely with data scientists, engineers, and other stakeholders to understand business requirements, develop ML models, and integratethem into products and services.

- Model Evaluation : Conduct model evaluation using statistical tests, cross-validation, and A/B testing to ensure reliability and generalizability.

- Documentation & Reporting : Maintain thorough documentation of processes, models, and systems. Provide insights and recommendations based on model results to stakeholders.

- Code Reviews & Best Practices : Participate in peer code reviews, and ensure adherence to coding best practices, including version control (Git), testing, and continuous integration.

- Stay Updated on Industry Trends : Keep abreast of new techniques and advancements in the field of machine learning, and suggest improvements for internal processes an models.

Required Skills & Qualifications :

- Education : Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or related field.

- Experience : 3 to 5 years of hands-on experience working as a machine learning engineer or in a related role.

- Programming Languages : Proficiency in Python (preferred), R, or Java. Experience with ML libraries such as TensorFlow, PyTorch, Scikit-learn, and Keras.

- Data Manipulation : Strong knowledge of SQL and experience working with large datasets (i.e. using tools like Pandas, NumPy, Spark).

- Cloud Services : Experience with cloud platforms like AWS, Google Cloud, or Azure, particularly with ML services such as SageMaker or AI Platform.

- Model Deployment : Hands-on experience with deploying ML models using Docker, Kubernetes, and CI/CD pipelines.

- Problem-Solving Skills : Strong analytical and problem-solving skills with the ability to understand complex data problems and implement effective solutions.

- Mathematics and Statistics : A solid foundation in mathematical concepts related to ML, such as linear algebra, probability, statistics, and optimization techniques.

- Communication Skills : Strong verbal and written communication skills to collaborate effectively with cross-functional teams and stakeholders.

Preferred Qualifications :

- Experience with deep learning frameworks (i.e., TensorFlow, PyTorch).

- Exposure to natural language processing (NLP), computer vision, or recommendation systems.

- Familiarity with version control systems (i.e., Git) and collaborative workflows.

- Experience with model interpretability and fairness techniques.

- Familiarity with big data tools (i.e., Hadoop, Spark, Kafka)