Data Scientist

Overview

Build ML models that solve real business problems: demand forecasting, customer churn prediction, dynamic pricing, fraud detection. You'll own the full cycle: problem framing, data analysis, model development, deployment, monitoring. This isn't Kaggle. You'll work with messy data, shifting requirements, and stakeholders who don't speak ML. Success means building models that actually get used and drive measurable business value.

Responsibilities

• •Frame business problems as ML tasks and define success metrics
• •Explore and analyze client data to identify patterns and opportunities
• •Build, train, and evaluate ML models
• •Deploy models to production and monitor performance
• •Communicate findings and recommendations to business stakeholders
• •Collaborate with engineers on data pipelines and model serving

Requirements

• •3+ years building ML models in production environments
• •Strong proficiency in Python (pandas, scikit-learn, PyTorch/TensorFlow)
• •Experience with A/B testing and experimental design
• •Track record of models deployed and monitored in production

Required Skills

Preferred Qualifications

• •Domain expertise in recommendation systems, forecasting, or NLP
• •Experience with model deployment and MLOps
• •Worked on high-impact business problems (revenue, cost, risk)
• •Strong communication skills with non-technical stakeholders

Nice to Have

A Day in the Life

Morning: Investigate model performance degradation in production. Mid-morning: Feature engineering for new churn model. Afternoon: Present ROI analysis to client marketing team. Evening: Code review and model documentation.

Why This Role

Work on diverse problems across industries. See your models drive real decisions. Fast feedback loops. Learn from experienced data scientists and ML engineers.

Frequently Asked Questions