Data Scientist

Role overview

Own the end-to-end data science lifecycle—transforming complex datasets into scalable predictive models and actionable insights. Partner with data engineering, product, and software teams to ensure models perform reliably in production.

Responsibilities

• Develop and deploy advanced ML/statistical models (classification, regression, clustering) using Python with scikit-learn or PyTorch
• Design and optimize data pipelines with SQL and PySpark to extract, clean, and transform large-scale enterprise data
• Conduct rigorous A/B testing and experimentation analysis to evaluate product and feature impact
• Collaborate with engineering teams to integrate models into production systems via REST APIs and cloud infrastructure
• Monitor model performance, detect model drift, and retrain models regularly

Requirements

• 3–6 years of professional experience in data science/quantitative analysis/applied machine learning
• Strong proficiency in Python, SQL, and core libraries: Pandas, NumPy, scikit-learn
• Experience deploying models into production using cloud environments (AWS, GCP, or Azure)
• Solid foundation in statistical analysis, hypothesis testing, and experimental design
• Bachelor’s or Master’s degree in Statistics, Computer Science, Mathematics, or a related quantitative field

Nice to have

• Experience with LLMs, NLP, vector databases, or real-time streaming data architectures