Data Scientist

Key Responsibilities

• Design, build, and validate machine learning models to solve business problems such as forecasting, segmentation, and recommendation
• Partner with product and engineering teams to translate business questions into well-scoped analytical problems
• Conduct exploratory data analysis to uncover trends, patterns, and actionable insights in large datasets
• Design and analyze A/B tests and other experiments to evaluate product and business decisions
• Build and maintain data pipelines in collaboration with data engineering for reliable, reproducible analysis
• Communicate findings clearly to both technical and non-technical stakeholders through reports, dashboards, and presentations
• Monitor deployed models for performance and drift, and iterate as needed
• Contribute to best practices around experimentation, model evaluation, and data quality

Requirements

• Python
• Pandas
• NumPy
• Scikit-learn
• SQL
• Statistics & Experiment Design (A/B Testing)
• Machine Learning Model Development

Preferred Skills

• Data Visualization (Matplotlib/Seaborn/Tableau)
• TensorFlow
• PyTorch
• AWS SageMaker
• GCP Vertex AI
• Azure ML
• Model Deployment to Production
• MLOps (experiment tracking, model monitoring)
• Domain Expertise (finance/healthcare/e-commerce)
• Stakeholder Communication

Qualifications

• 3+ years of experience in a data science, applied ML, or quantitative analytics role
• Strong proficiency in Python (Pandas, NumPy, Scikit-learn) and SQL
• Solid grounding in statistics and experiment design, including A/B testing methodology
• Experience building and evaluating machine learning models for real-world problems
• Ability to communicate technical findings clearly to non-technical audiences
• Experience working with large datasets and writing efficient, production-quality analysis code
• Familiarity with data visualization tools (Matplotlib, Seaborn, Tableau, or similar)

Benefits

• Competitive salary and performance-based bonuses
• Flexible working hours and remote/hybrid options
• Health insurance and wellness benefits
• Learning and development budget
• A collaborative environment where your analysis directly shapes decisions