Data Scientist

As a Data Scientist in this role, you will be responsible for developing and implementing machine learning models for predictive analytics, classification, and optimization. Your key responsibilities will include working with large datasets to perform data cleaning, preprocessing, and feature engineering. You will collaborate with cross-functional teams to translate business requirements into data-driven solutions and deploy ML models on cloud platforms (AWS, Azure, GCP) ensuring scalability and performance. Additionally, you will conduct model evaluation, tuning, and validation to ensure accuracy and reliability, and present insights and recommendations to stakeholders using data visualization tools. It is essential for you to stay updated with emerging trends in AI/ML, cloud computing, and big data technologies. Key Responsibilities: - Develop and implement machine learning models for predictive analytics, classification, and optimization. - Work with large datasets to perform data cleaning, preprocessing, and feature engineering. - Collaborate with cross-functional teams to translate business requirements into data-driven solutions. - Deploy ML models on cloud platforms (AWS, Azure, GCP) ensuring scalability and performance. - Conduct model evaluation, tuning, and validation to ensure accuracy and reliability. - Present insights and recommendations to stakeholders using data visualization tools. - Stay updated with emerging trends in AI/ML, cloud computing, and big data technologies. Qualifications Required: - Strong proficiency in Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch). - Hands-on experience with AI/ML algorithms and statistical modeling. - Expertise in cloud platforms (AWS, Azure, GCP) for ML deployment. - Solid understanding of data structures, algorithms, and distributed computing. - Experience with SQL/NoSQL databases and data pipelines. - Strong problem-solving and analytical skills. - Bachelor's/master's degree in computer science, Data Science, or related field. Additional Company Details: The company prefers candidates with experience in MLOps frameworks (Kubeflow, MLflow, Airflow), knowledge of big data tools (Spark, Hadoop), familiarity with deep learning architectures (CNNs, RNNs, Transformers), and strong communication and stakeholder management skills. As a Data Scientist in this role, you will be responsible for developing and implementing machine learning models for predictive analytics, classification, and optimization. Your key responsibilities will include working with large datasets to perform data cleaning, preprocessing, and feature engineering. You will collaborate with cross-functional teams to translate business requirements into data-driven solutions and deploy ML models on cloud platforms (AWS, Azure, GCP) ensuring scalability and performance. Additionally, you will conduct model evaluation, tuning, and validation to ensure accuracy and reliability, and present insights and recommendations to stakeholders using data visualization tools. It is essential for you to stay updated with emerging trends in AI/ML, cloud computing, and big data technologies. Key Responsibilities: - Develop and implement machine learning models for predictive analytics, classification, and optimization. - Work with large datasets to perform data cleaning, preprocessing, and feature engineering. - Collaborate with cross-functional teams to translate business requirements into data-driven solutions. - Deploy ML models on cloud platforms (AWS, Azure, GCP) ensuring scalability and performance. - Conduct model evaluation, tuning, and validation to ensure accuracy and reliability. - Present insights and recommendations to stakeholders using data visualization tools. - Stay updated with emerging trends in AI/ML, cloud computing, and big data technologies. Qualifications Required: - Strong proficiency in Python (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch). - Hands-on experience with AI/ML algorithms and statistical modeling. - Expertise in cloud platforms (AWS, Azure, GCP) for ML deployment. - Solid understanding of data structures, algorithms, and distributed computing. - Experience with SQL/NoSQL databases and data pipelines. - Strong problem-solving and analytical skills. - Bachelor's/master's degree in computer science, Data Science, or related field. Additional Company Details: The company prefers candidates with experience in MLOps frameworks (Kubeflow, MLflow, Airflow), knowledge of big data tools (Spark, Hadoop), familiarity with deep learning architectures (CNNs, RNNs, Transformers), and strong communication and stakeholder management skills.