Job
Description
Role Overview: As a Data Scientist Engineer, you will be responsible for designing and implementing machine learning models to address business challenges effectively. You will clean, preprocess, and analyze large-scale datasets for extracting insights and engineering model features. Additionally, you will train and fine-tune ML models using various techniques, evaluate model performance, deploy models into production environments, and collaborate with cross-functional teams to integrate ML solutions. Your role will also involve staying updated with the latest trends in AI/ML, contributing to knowledge sharing, and maintaining thorough documentation of models and processes. Key Responsibilities: - Design and implement machine learning models to address business challenges effectively. - Clean, preprocess, and analyze large-scale datasets for extracting insights and engineering model features. - Train and fine-tune ML models using deep learning, regression, classification, and ensemble techniques. - Evaluate model performance and improve accuracy, efficiency, and scalability. - Deploy ML models into production environments; monitor and retrain models as needed. - Collaborate with engineers, data scientists, and stakeholders to integrate ML solutions. - Stay current with the latest trends in AI/ML and contribute to knowledge sharing. - Maintain thorough documentation of models, workflows, and processes. Qualifications: - Bachelors or Masters degree in Computer Science, Data Science, Machine Learning, or a related field - 6-10 years of industry experience in data science, machine learning, or related domains Must-Have Skills: - Experience in Time Series Forecasting, Regression, and Classification models - Proficient in Python, R*, and Data Analysis* - Strong experience with Pandas, NumPy, Matplotlib for large data handling - Version control tools like Git - ML Frameworks: TensorFlow, PyTorch, Scikit-learn, Keras - Exposure to Cloud Platforms: AWS / Azure / GCP - Experience with Docker and Kubernetes - Hands-on in Data Collection, Feature Engineering, Model Selection, Evaluation, and Deployment Good to Have: - Exposure to Big Data tools such as Hadoop, Spark - BFSI / Banking domain experience - Familiarity with additional AI/ML libraries and tools beyond the mentioned frameworks Additional Details: - Job Type: Contractual / Temporary - Contract Length: 6 months - Work Location: In person Role Overview: As a Data Scientist Engineer, you will be responsible for designing and implementing machine learning models to address business challenges effectively. You will clean, preprocess, and analyze large-scale datasets for extracting insights and engineering model features. Additionally, you will train and fine-tune ML models using various techniques, evaluate model performance, deploy models into production environments, and collaborate with cross-functional teams to integrate ML solutions. Your role will also involve staying updated with the latest trends in AI/ML, contributing to knowledge sharing, and maintaining thorough documentation of models and processes. Key Responsibilities: - Design and implement machine learning models to address business challenges effectively. - Clean, preprocess, and analyze large-scale datasets for extracting insights and engineering model features. - Train and fine-tune ML models using deep learning, regression, classification, and ensemble techniques. - Evaluate model performance and improve accuracy, efficiency, and scalability. - Deploy ML models into production environments; monitor and retrain models as needed. - Collaborate with engineers, data scientists, and stakeholders to integrate ML solutions. - Stay current with the latest trends in AI/ML and contribute to knowledge sharing. - Maintain thorough documentation of models, workflows, and processes. Qualifications: - Bachelors or Masters degree in Computer Science, Data Science, Machine Learning, or a related field - 6-10 years of industry experience in data science, machine learning, or related domains Must-Have Skills: - Experience in Time Series Forecasting, Regression, and Classification models - Proficient in Python, R*, and Data Analysis* - Strong experience with Pandas, NumPy, Matplotlib for large data handling - Version control tools like Git - ML Frameworks: TensorFlow, PyTorch, Scikit-learn, Keras - Exposure to Cloud Platforms: AWS / Azure / GCP - Experience with Docker and Kubernetes - Hands-on in Data Collection, Feature Engineering, Model Selection, Evaluation, and Deployment Good to Have: - Exposure to Big Data tools such as Hadoop, Spark - BFSI / Banking domain experience - Familiarity with additional AI/ML libraries and tools beyond the mentioned frameworks Additional Details: - Job Type: Contractual / Temporary - Contract Length: 6 months - Work Location: In person
