Support the development, training, and validation of machine learning models focused on customer acquisition, lead scoring, response modeling, and propensity modeling.
Work with structured datasets to perform data preparation, feature engineering, and exploratory analysis.
Assist in building, testing, and deploying models using Python and ML libraries (e.g., scikit‑learn, XGBoost, LightGBM, etc.).
Contribute to the development of AI‑enabled tools, reusable scripts, and modeling accelerators to improve modeling efficiency and consistency.
Collaborate with senior data scientists to translate business problems into analytical solutions.
Support model performance monitoring, documentation, and basic explainability analysis.
Partner with technology and engineering teams to ensure models can be operationalized and scaled.
Hands‑on exposure to AI‑driven model development, automation, and analytical tool building.
Opportunity to contribute to internal AI utilities, notebooks, APIs, or model frameworks that enhance productivity.
Work in modern analytics environments using cloud platforms, notebooks, and version control (e.g., Git).
Requirements
0–2 years of experience (or relevant internships/projects) in data science, analytics, or modeling.
Strong foundation in statistics, machine learning concepts, and data analysis.
Proficiency in Python for data analysis and modeling.
Familiarity with SQL and working with large datasets.
Basic understanding of supervised ML models used in acquisition or marketing analytics.
Strong analytical thinking and problem‑solving skills.