Senior Data Scientist

This is a remote position.

Key Responsibilities

• Lead end-to-end data science projects from problem definition to deployment and business impact measurement.
  
• Build predictive models, statistical models, and machine learning solutions for business and product use cases.
  
• Perform exploratory data analysis to identify trends, patterns, and actionable insights.
  
• Design and implement data pipelines for large-scale structured and unstructured datasets.
  
• Develop and maintain data models, feature engineering pipelines, and experimentation frameworks.
  
• Work closely with engineering teams to deploy models into production environments.
  
• Build dashboards and reporting systems to communicate insights to stakeholders.
  
• Design and run A/B tests, hypothesis testing, and causal analysis to guide decision-making.
  
• Work with big data technologies to process and analyze large datasets efficiently.
  
• Ensure data quality, integrity, and governance across systems.
  
• Collaborate with product, engineering, and business teams to define metrics and success KPIs.
  
• Mentor junior data scientists and guide best practices in analytics and modeling.
  

Requirements

Technical Requirements

• 5–8+ years of experience in Data Science, Machine Learning, or Analytics roles
  
• Strong programming skills in Python or R
  
• Strong knowledge of SQL and relational databases (PostgreSQL, MySQL, etc.)
  
• Experience with machine learning libraries (Scikit-learn, XGBoost, TensorFlow, PyTorch, etc.)
  
• Strong foundation in statistics, probability, and linear algebra
  
• Experience with data visualization tools (Tableau, Power BI, or similar)
  
• Experience working with large datasets and distributed systems (Spark, Hadoop, etc.)
  
• Understanding of data pipelines and ETL processes
  
• Experience deploying models into production environments
  

Nice to Have

• Experience with cloud platforms (AWS, GCP, Azure)
  
• Experience with real-time data processing systems
  
• Familiarity with MLOps practices (model monitoring, CI/CD for ML)
  
• Experience with feature stores and data versioning systems
  
• Knowledge of experimentation platforms and causal inference
  
• Experience working in product-driven or high-growth environments