Data Scientist

We are looking for a curious and driven Data Scientist to join our growing analytics team. In this role you will turn complex, large-scale datasets into actionable insights and production-ready machine learning models that directly influence product strategy and business decisions. You will collaborate closely with engineering, product, and business stakeholders to frame problems, design experiments, and deliver measurable impact.

Responsibilities:

• Design, develop, and deploy machine learning models and statistical algorithms across classification, regression, clustering, and recommendation use cases.
• Conduct exploratory data analysis (EDA) to surface patterns, anomalies, and opportunities from structured and unstructured data.
• Build and maintain data pipelines and feature engineering workflows in collaboration with data engineering teams.
• Define and run A/B tests and causal inference studies; translate results into clear recommendations for product and business teams.
• Communicate findings to both technical peers and non-technical stakeholders through compelling visualizations and narratives.
• Monitor model performance in production, identify drift or degradation, and iterate on improvements.
• Document methodologies, model cards, and technical decisions to maintain reproducibility and knowledge sharing.
• Mentor junior analysts and data scientists, providing code reviews and guidance on best practices.

Requirements:

• 3-6 years of hands-on experience in a data science, machine learning, or applied research role.
• Bachelor's or Master's degree in Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field.
• Strong proficiency in Python (pandas, scikit-learn, NumPy, matplotlib / seaborn) and SQL.
• Solid grounding in machine learning fundamentals: model selection, regularization, cross-validation, and bias-variance trade-off.
• Experience with one or more deep learning frameworks (TensorFlow or PyTorch) for at least one production use case.
• Demonstrated ability to own an end-to-end ML project from problem definition and data collection through deployment and monitoring.
• Comfortable working with cloud platforms (AWS, GCP, or Azure) and distributed data tools (Spark, BigQuery, or Redshift).
• Strong statistical reasoning: hypothesis testing, Bayesian inference, regression analysis, and time-series modeling.

Preferred Qualifications:

• Experience with MLOps tools and practices (MLflow, Kubeflow, Airflow, Docker, CI/CD pipelines).
• Familiarity with NLP, computer vision, or large language model (LLM) fine-tuning and prompt engineering.
• Exposure to causal inference methods (propensity scoring, diff-in-diff, instrumental variables).