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