Data Scientist- AI/ML

As a Data Scientist Engineer at our company, you will be responsible for the following key areas: Role Overview: You will be expected to utilize your robust experience in AI/ML, data collection preprocessing, estimation, and architecture creation to design and implement ML models that address complex business challenges. Your primary responsibilities will involve cleaning, preprocessing, and analyzing large datasets to derive meaningful insights, as well as training and fine-tuning ML models using various techniques. Additionally, you will be involved in model performance evaluation, optimization, deployment in production, and collaboration with various teams to integrate ML solutions. Key Responsibilities: - Model Development: Design and implement ML models to tackle complex business challenges. - Data Preprocessing: Clean, preprocess, and analyze large datasets for meaningful insights and model features. - Model Training: Train and fine-tune ML models using various techniques including deep learning and ensemble methods. - Evaluation and Optimization: Assess model performance, optimize for accuracy, efficiency, and scalability. - Deployment: Deploy ML models in production, monitor performance for reliability. - Collaboration: Work with data scientists, engineers, and stakeholders to integrate ML solutions. - Research: Stay updated on ML/AI advancements, contribute to internal knowledge. - Documentation: Maintain comprehensive documentation for all ML models and processes. Qualifications Required: - Bachelor's or master's degree in computer science, Machine Learning, Data Science, or a related field. - 6-10 years of experience in the field. Desirable Skills: Must Have: - Experience in timeseries forecasting, regression Model, Classification Model. - Proficiency in Python, R, and data analysis. - Handling large datasets with Panda, Numpy, and Matplotlib. - Version Control: Git or any other. - Hands-on experience in ML Frameworks like Tensorflow, PyTorch, Scikit-Learn, Keras. - Good knowledge of Cloud platforms (AWS/Azure/GCP), Docker, and Kubernetes. - Expertise in model selection, evaluation, deployment, data collection and preprocessing, and feature engineering. Good to Have: - Experience with Big Data and analytics using technologies like Hadoop, Spark, etc. - Additional experience or knowledge in AI/ML technologies beyond the mentioned frameworks. - Experience in the BFSI and banking domain. If you are interested in this exciting opportunity, please share your resume at the provided email address. As a Data Scientist Engineer at our company, you will be responsible for the following key areas: Role Overview: You will be expected to utilize your robust experience in AI/ML, data collection preprocessing, estimation, and architecture creation to design and implement ML models that address complex business challenges. Your primary responsibilities will involve cleaning, preprocessing, and analyzing large datasets to derive meaningful insights, as well as training and fine-tuning ML models using various techniques. Additionally, you will be involved in model performance evaluation, optimization, deployment in production, and collaboration with various teams to integrate ML solutions. Key Responsibilities: - Model Development: Design and implement ML models to tackle complex business challenges. - Data Preprocessing: Clean, preprocess, and analyze large datasets for meaningful insights and model features. - Model Training: Train and fine-tune ML models using various techniques including deep learning and ensemble methods. - Evaluation and Optimization: Assess model performance, optimize for accuracy, efficiency, and scalability. - Deployment: Deploy ML models in production, monitor performance for reliability. - Collaboration: Work with data scientists, engineers, and stakeholders to integrate ML solutions. - Research: Stay updated on ML/AI advancements, contribute to internal knowledge. - Documentation: Maintain comprehensive documentation for all ML models and processes. Qualifications Required: - Bachelor's or master's degree in computer science, Machine Learning, Data Science, or a related field. - 6-10 years of experience in the field. Desirable Skills: Must Have: - Experience in timeseries forecasting, regression Model, Classification Model. - Proficiency in Python, R, and data analysis. - Handling large datasets with Panda, Numpy, and Matplotlib. - Version Control: Git or any other. - Hands-on experience in ML Frameworks like Tensorflow, PyTorch, Scikit-Learn, Keras. - Good knowledge of Cloud platforms (AWS/Azure/GCP), Docker, and Kubernetes. - Expertise in model selection, evaluation, deployment, data collection and preprocessing, and feature engineering. Good to Have: - Experience with Big Data and analytics using technologies like Hadoop, Spark, etc. - Additional experience or knowledge in AI/ML technologies beyond the mentioned frameworks. - Experience in the BFSI and banking domain. If