Trainee Engineer-Data science
Cyient
Date: 1 week ago
City: Allen, TX
Contract type: Full time
JD for Trainee Engineer -Data Science :
Data Collection and Preparation
Data Gathering: Collecting data from various sources, including databases, APIs, and web scraping.
Data Cleaning: Identifying and correcting errors or inconsistencies in data to ensure quality.
Data Transformation: Converting raw data into a usable format, including normalization and scaling.
Exploratory Data Analysis (EDA)
Descriptive Statistics: Calculating measures such as mean, median, mode, variance, and standard deviation.
Visualization: Creating charts, graphs, and plots to understand data distributions and relationships.
Pattern Identification: Detecting trends, anomalies, and patterns in data.
Model Development
Algorithm Selection: Choosing appropriate machine learning algorithms based on the problem at hand.
Model Training: Training models using supervised, unsupervised, or reinforcement learning techniques.
Model Evaluation: Assessing model performance using metrics like accuracy, precision, recall, F1 score, and ROC-AUC.
Deployment and Maintenance
Model Deployment: Implementing models in production environments.
Monitoring: Tracking model performance and making adjustments as needed.
Documentation: Writing clear documentation for data processes, models, and code.
Collaboration and Communication
Team Collaboration: Working with other data scientists, engineers, and stakeholders to understand requirements and deliver solutions.
Reporting: Presenting findings and insights to non-technical stakeholders through reports and presentations.
Continuous Learning: Staying updated with the latest trends and technologies in data science.
Technical Skills Development
Programming: Writing code in languages such as Python, R, SQL, and sometimes Java or Scala.
Tools and Libraries: Using data science tools and libraries like Pandas, NumPy, Scikit-learn, TensorFlow, and PyTorch.
Database Management: Working with SQL and NoSQL databases to store and retrieve data.
Ethical Considerations
Data Privacy: Ensuring compliance with data privacy laws and regulations.
Bias Mitigation: Identifying and mitigating biases in data and models.
Data Collection and Preparation
Data Gathering: Collecting data from various sources, including databases, APIs, and web scraping.
Data Cleaning: Identifying and correcting errors or inconsistencies in data to ensure quality.
Data Transformation: Converting raw data into a usable format, including normalization and scaling.
Exploratory Data Analysis (EDA)
Descriptive Statistics: Calculating measures such as mean, median, mode, variance, and standard deviation.
Visualization: Creating charts, graphs, and plots to understand data distributions and relationships.
Pattern Identification: Detecting trends, anomalies, and patterns in data.
Model Development
Algorithm Selection: Choosing appropriate machine learning algorithms based on the problem at hand.
Model Training: Training models using supervised, unsupervised, or reinforcement learning techniques.
Model Evaluation: Assessing model performance using metrics like accuracy, precision, recall, F1 score, and ROC-AUC.
Deployment and Maintenance
Model Deployment: Implementing models in production environments.
Monitoring: Tracking model performance and making adjustments as needed.
Documentation: Writing clear documentation for data processes, models, and code.
Collaboration and Communication
Team Collaboration: Working with other data scientists, engineers, and stakeholders to understand requirements and deliver solutions.
Reporting: Presenting findings and insights to non-technical stakeholders through reports and presentations.
Continuous Learning: Staying updated with the latest trends and technologies in data science.
Technical Skills Development
Programming: Writing code in languages such as Python, R, SQL, and sometimes Java or Scala.
Tools and Libraries: Using data science tools and libraries like Pandas, NumPy, Scikit-learn, TensorFlow, and PyTorch.
Database Management: Working with SQL and NoSQL databases to store and retrieve data.
Ethical Considerations
Data Privacy: Ensuring compliance with data privacy laws and regulations.
Bias Mitigation: Identifying and mitigating biases in data and models.
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