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About the role
Job Description: Role Overview - We are seeking a Data Scientist who can bridge the gap between data engineering, analytics, and machine learning. This role requires expertise in building data pipelines, analyzing complex datasets, and deploying AI models in production. If you are passionate about working on end-to-end data solutions, this role is for you! Key Responsibilities - Data Engineering: - Design and develop scalable ETL (Extract, Transform, Load) pipelines. - Work with big data technologies (Hadoop, Spark, Kafka, Snowflake) for data processing. - Ensure data integrity, governance, and security across multiple sources. Data Analysis & Visualization: - Perform exploratory data analysis (EDA) to identify patterns and trends. - Create dashboards and reports using Tableau, Power BI, or Looker. - Optimize SQL queries for faster data retrieval. Machine Learning & AI: - Develop and deploy machine learning models for real-time decision-making. - Use ML frameworks like TensorFlow, PyTorch, or Scikit-learn. - Implement MLOps practices (model monitoring, CI/CD for ML, A/B testing). - Work with APIs and cloud-based AI services for model deployment. Required Skills & Qualifications - Bachelor's or Masters degree in Data Science, Computer Science, AI, or related fields. - Solid programming skills in Python, SQL, R, Scala, or Java. - Experience with Big Data frameworks (Hadoop, Spark, Kafka, Airflow). - Hands-on experience with cloud platforms (AWS, GCP, Azure) and containerization tools (Docker, Kubernetes). - Strong understanding of machine learning, deep learning, and statistical analysis. Preferred Qualifications - Experience with MLOps tools (MLflow, Kubeflow, Airflow). - Knowledge of data lakes and warehouses (Snowflake, Redshift, BigQuery). - Exposure to real-time data streaming and NLP models. Job Description: Role Overview - We are seeking a Data Scientist who can bridge the gap between data engineering, analytics, and machine learning. This role requires expertise in building data pipelines, analyzing complex datasets, and deploying AI models in production. If you are passionate about working on end-to-end data solutions, this role is for you! Key Responsibilities - Data Engineering: - Design and develop scalable ETL (Extract, Transform, Load) pipelines. - Work with big data technologies (Hadoop, Spark, Kafka, Snowflake) for data processing. - Ensure data integrity, governance, and security across multiple sources. Data Analysis & Visualization: - Perform exploratory data analysis (EDA) to identify patterns and trends. - Create dashboards and reports using Tableau, Power BI, or Looker. - Optimize SQL queries for faster data retrieval. Machine Learning & AI: - Develop and deploy machine learning models for real-time decision-making. - Use ML frameworks like TensorFlow, PyTorch, or Scikit-learn. - Implement MLOps practices (model monitoring, CI/CD for ML, A/B testing). - Work with APIs and cloud-based AI services for model deployment. Required Skills & Qualifications - Bachelor's or Masters degree in Data Science, Computer Science, AI, or related fields. - Solid programming skills in Python, SQL, R, Scala, or Java. - Experience with Big Data frameworks (Hadoop, Spark, Kafka, Airflow). - Hands-on experience with cloud platforms (AWS, GCP, Azure) and containerization tools (Docker, Kubernetes). - Strong understanding of machine learning, deep learning, and statistical analysis. Preferred Qualifications - Experience with MLOps tools (MLflow, Kubeflow, Airflow). - Knowledge of data lakes and warehouses (Snowflake, Redshift, BigQuery). - Exposure to real-time data streaming and NLP models.