Data Scientist

About the Company

Capgemini Engineering is a global leader in engineering and R&D services, specializing in innovation and technology solutions across various industries.

About the Role

• Proficiency in SQL and database management
• Knowledge of data engineering concepts, including data pipelines, integration, warehousing, and architecture, is beneficial.
• Cloud computing platforms (Azure) and services like Azure Machine Learning can be advantageous.
• Proficiency in version control systems like Git is crucial for effective collaboration and code management.

Responsibilities

• Proficiency in programming languages like Python
• Skills in handling and analysing large datasets, including data cleaning, wrangling, transformation, and exploratory data analysis (EDA) using tools like pandas.
• Knowledge of statistical concepts such as hypothesis testing, regression analysis, ANOVA, experimental design, and probability theory.
• Understanding machine learning algorithms, supervised and unsupervised learning, feature engineering, model selection and evaluation, and hyperparameter tuning is essential, with libraries like scikit-learn, TensorFlow, or PyTorch commonly used. Knowledge of YOLO is desirable
• Data visualisation skills using libraries like Matplotlib, Seaborn, or ggplot
• Familiarity with big data technologies like Apache Hadoop, Apache Spark, or distributed databases enables the processing and analysis of large-scale datasets.
• Proficiency in SQL and database management
• Knowledge of data engineering concepts, including data pipelines, integration, warehousing, and architecture, is beneficial.
• Cloud computing platforms (Azure) and services like Azure Machine Learning can be advantageous.
• Proficiency in version control systems like Git is crucial for effective collaboration and code management.

Qualifications

BE/BTech/MTech

Required Skills

• Proficiency in programming languages like Python
• Skills in handling and analysing large datasets, including data cleaning, wrangling, transformation, and exploratory data analysis (EDA) using tools like pandas.
• Knowledge of statistical concepts such as hypothesis testing, regression analysis, ANOVA, experimental design, and probability theory.

Preferred Skills

• Proficiency in SQL and database management
• Knowledge of data engineering concepts, including data pipelines, integration, warehousing, and architecture, is beneficial.
• Cloud computing platforms (Azure) and services like Azure Machine Learning can be advantageous.
• Proficiency in version control systems like Git is crucial for effective collaboration and code management.