Data Scientist

The role owns the end-to-end data science lifecycle, turning complex datasets into scalable, production-grade predictive models and actionable insights that drive core business growth.

You will collaborate closely with data engineers, product managers, and software teams to build high-performance algorithms that operate reliably at scale.

Key Responsibilities

• Design, train, and validate advanced machine learning and statistical models using Python and SQL for large-scale production environments
• Partner with data engineering teams to build robust feature stores and scalable data ingestion pipelines
• Deploy, monitor, and iterate on models using cloud platforms and MLOps tools to ensure high availability and prevent performance degradation
• Conduct rigorous exploratory data analysis to uncover hidden patterns, trends, and opportunities for product optimization
• Design and analyze A/B tests to measure the real-world business impact of deployed algorithms and features
• Communicate complex technical findings clearly to both technical and non-technical stakeholders across the organization

• 3–6 years of professional experience in data science, quantitative analysis, or applied machine learning
• Expert-level proficiency in Python, SQL, and core data science libraries such as Pandas, NumPy, scikit-learn, and XGBoost
• Proven track record of deploying and maintaining machine learning models in production cloud environments (AWS, GCP, or Azure)
• Strong foundation in statistical modeling, hypothesis testing, experiment design, and evaluation metrics
• Bachelor's or Master's degree in Statistics, Computer Science, Mathematics, Economics, or a related quantitative field
• Bonus: Experience with deep learning frameworks (PyTorch/TensorFlow) or large language models in a production setting