Job Description
NLP Predictive Analyst Engineer Job Description
Responsibilities:
Predictive Modeling:
Develop and implement predictive models using machine learning algorithms.
Analyze and interpret data to identify patterns, trends, and insights for predictive purposes.
Natural Language Processing (NLP):
Apply NLP techniques to extract meaningful information from unstructured text data.
Build and refine models for sentiment analysis, entity recognition, and document classification.
Technical Skills:
Strong programming skills in languages Python, secondary would be Java, or C++.
Familiarity with Artificial intelligence and machine learning technologies.
Feature Engineering:
Identify and engineer relevant features from structured and unstructured data.
Optimize feature selection to improve model performance and interpretability.
Data Preprocessing and Cleaning:
Clean and preprocess data to ensure its suitability for predictive modeling.
Handle missing data, outliers, and other data quality issues.
Model Evaluation and Interpretation:
Evaluate model performance using appropriate metrics and statistical methods.
Interpret model results, providing actionable insights and recommendations.
Continuous Learning and Innovation:
Stay informed about the latest advancements in AI, ML, and NLP.
Explore innovative approaches and technologies to enhance predictive analytics capabilities.
Collaboration with Stakeholders:
Collaborate with business stakeholders to understand requirements and goals.
Communicate complex analytical findings in a clear and understandable manner.
Deployment of Predictive Models:
Work with development teams to deploy predictive models into production environments.
Ensure the seamless integration of models into existing business processes.
Documentation:
Document the entire predictive modeling process, including data sources, methodology, and results.
Provide documentation for end-users and stakeholders.
Qualifications/Experience:
Proven experience as an AI/ML Predictive Analyst or similar role.
Strong proficiency in machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch).
Knowledge of NLP techniques and tools.
Experience with feature engineering and model deployment.
Nice to Haves:
Familiarity with cloud-based AI/ML services (e.g., AWS SageMaker, Azure ML).
Understanding of deep learning architectures.
Experience with time-series analysis for predictive modeling.