[Hiring] Feature Engineer @ASK Staffing

Role Description

We are seeking a Feature Engineer to design, develop, and optimize high-quality features for machine learning and artificial intelligence (AI) models. The ideal candidate will transform raw data into meaningful, predictive features that improve model performance, scalability, and reliability. This role requires expertise in data preprocessing, feature extraction, feature selection, statistical analysis, and collaboration with data scientists, machine learning engineers, and data engineers to build production-ready AI solutions.

Key Responsibilities

• Analyze structured and unstructured data to identify meaningful features for machine learning models.
• Design and implement feature engineering pipelines for classification, regression, recommendation, forecasting, and NLP applications.
• Perform data cleaning, transformation, normalization, encoding, aggregation, and feature extraction.
• Develop and maintain reusable feature stores and feature management workflows.
• Engineer features from text, images, time-series, geospatial, sensor, and transactional datasets as applicable.
• Evaluate feature importance using statistical methods and machine learning techniques.
• Collaborate with data scientists to improve model accuracy through feature optimization.
• Automate feature generation, validation, and monitoring in production environments.
• Ensure consistency, versioning, and governance of engineered features across ML workflows.
• Optimize feature pipelines for scalability, performance, and real-time inference.
• Document feature engineering methodologies, assumptions, and best practices.
• Stay current with advancements in automated feature engineering, feature stores, and AI technologies.

Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, Statistics, Mathematics, Engineering, or a related field.
• 2–6+ years of experience in machine learning, data engineering, feature engineering, or data science.
• Strong proficiency in Python and SQL.
• Experience with data preprocessing, statistical analysis, and machine learning workflows.
• Hands-on experience with libraries such as Pandas, NumPy, and Scikit-learn.
• Knowledge of feature selection, dimensionality reduction, and model evaluation techniques.
• Familiarity with data pipelines, ETL processes, and distributed data processing.
• Strong analytical and problem-solving skills.

Preferred Qualifications

• Experience with feature stores such as Feast or Tecton.
• Familiarity with Apache Spark, Databricks, or distributed computing platforms.
• Experience with NLP, computer vision, time-series, or recommendation systems.
• Knowledge of MLOps, model deployment, and monitoring.
• Experience working with cloud platforms such as AWS, Microsoft Azure, or Google Cloud.
• Relevant certifications in AI, machine learning, cloud computing, or data engineering.

Technical Skills

• Python
• SQL
• Pandas
• NumPy
• Scikit-learn
• Feature Engineering
• Feature Selection
• Feature Extraction
• Data Preprocessing
• Statistical Analysis
• ETL Pipelines
• Apache Spark
• Databricks
• Feast
• Tecton
• MLflow
• Airflow
• TensorFlow (preferred)
• PyTorch (preferred)
• Docker
• Git
• CI/CD
• AWS, Microsoft Azure, or Google Cloud

Soft Skills

• Analytical thinking
• Problem-solving
• Attention to detail
• Collaboration
• Communication
• Time management
• Critical thinking
• Adaptability
• Continuous learning

Key Deliverables

• Production-ready feature engineering pipelines
• High-quality engineered feature sets
• Feature store implementation and maintenance
• Feature documentation and metadata
• Model performance improvement reports
• Automated feature validation workflows
• Data quality assessments
• Technical documentation and best practices

Success Metrics

• Improvement in machine learning model accuracy and performance
• Quality and reusability of engineered features
• Reduction in feature pipeline processing time
• Reliability and scalability of feature engineering workflows
• Adoption of standardized feature engineering practices
• Reduction in data quality issues affecting model performance
• Successful integration of feature pipelines into production ML systems
• Timely delivery of feature engineering initiatives