The ideal candidate is a data-driven problem solver with hands-on experience in statistical modeling, predictive analytics, clustering algorithms, entity resolution, and basic MLOps. You should be comfortable working with large datasets, experimenting with machine learning techniques, and translating analytical insights into scalable business solutions.
Key Responsibilities
- Analyze large datasets to identify trends, patterns, and actionable insights using statistical methods.
- Develop, train, validate, and optimize supervised and unsupervised machine learning models.
- Design and implement clustering algorithms to identify similar entities, customer segments, or devices.
- Build entity resolution and fuzzy matching solutions to accurately link duplicate or related records across multiple datasets.
- Evaluate model performance using appropriate statistical and machine learning metrics.
- Conduct feature engineering, model tuning, and experimentation to improve prediction accuracy.
- Collaborate with cross-functional teams to understand business requirements and translate them into analytical solutions.
- Develop reproducible machine learning workflows and support model deployment using basic MLOps practices.
- Document methodologies, assumptions, and experimental findings.
Required Qualifications
- Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Artificial Intelligence, or a related field.
- 4+ years of experience in Data Science or Machine Learning.
- Solid programming skills in Python.
- Experience with SQL and data manipulation libraries such as Pandas and NumPy.
- Excellent problem-solving and analytical skills.