Data Science - Traditional AI / ML - Senior Associate

Role & responsibilities

Analyzing client business problems and translating exploratory findings into clear data science recommendations
- Building statistical models, machine learning workflows, and predictive solutions that address complex business questions
- Designing and refining data preparation, feature engineering, and model validation activities across project workstreams
- Interpreting structured and unstructured data sources to identify trends, patterns, and measurable drivers of performance
- Developing data visualizations and presentation materials that communicate findings to technical and nontechnical audiences
- Applying Python, R, SQL, and machine learning libraries to support experimentation, model tuning, and solution delivery
- Reviewing data quality, model inputs, and output logic to identify issues that affect analysis and recommendations
- Supporting client conversations by clarifying needs, documenting requirements, and adapting deliverables as priorities shift
- Guiding junior team members on analytical methods, problem solving approaches, and technical documentation
- Validating that work aligns with professional standards, project objectives, and data science methodologies

What Sets You Apart

• Preference for at least one of the following fields of study: Artificial Intelligence and Robotics, Business Analytics, Computer and Information Science, Computer Engineering, Computer Programming, Data Processing/Analytics/Science, Engineering, Information Technology, Machine Learning, Management Information Systems, Mathematics, Statistics, Systems Engineering
• Demonstrating AI and machine learning project experience in client settings
• Applying statistical analysis to interpret data and shape recommendations
• Translating complex technical concepts into clear client conversations
• Supporting workstreams while mentoring junior team members

• Using Python, R, TensorFlow, or Scikit-learn for modeling

• Advanced ML Models

• Feature Engineering

• Model Optimization

• Statistical Rigor

• Data Visualization

• Hypothesis testing

• Problem-solving Framework

• Python/SQL

• Ensemble Methods

• Regression Analysis

• Classification Techniques

• Clustering Methods

• Model Validation

• A/B Testing

• Causal Inference

• Performance Analysis

• Reproducibility

• Documentation

• Mentoring Capability

• Cross-functional Communication

• Business Translation