Kavi Global - Senior Data Scientist - Predictive Modeling

Overview

We are seeking a strategic and innovative Senior Data Scientist to join our high-performing Data Science team.

This role involves leading the design, development, and deployment of advanced analytics and machine learning solutions that drive measurable business impact.

You will collaborate closely with Product, Engineering, and Business teams to translate complex data into actionable insights and scalable data products.

Key Responsibilities

• Lead and deliver end-to-end data science projects, including problem formulation, data exploration, feature engineering, model development, validation, and production deployment.
• Develop and deploy predictive models, optimization techniques, and statistical analyses to solve real-world business problems.
• Communicate complex analytical insights through clear, compelling storytelling to both technical and non-technical stakeholders.
• Design and execute experimentation frameworks such as A/B testing to improve product performance and business outcomes.
• Collaborate with Data Engineering teams to build reliable, scalable data pipelines and production-ready ML models.
• Mentor and guide junior data scientists, promote best practices, and contribute to team knowledge sharing and research initiatives.
• Ensure models follow performance, scalability, and governance standards in production environments.
  
  

• Masters or PhD in Computer Science, Statistics, Mathematics, or a related field.
• 5+ years of hands-on experience in Data Science, including deploying models into production.
• Strong proficiency in Python and SQL.
• Hands-on experience with ML frameworks such as scikit-learn, TensorFlow, PyTorch, and XGBoost.
• Solid foundation in statistics, machine learning algorithms, and experimental design.
• Experience with data visualization tools such as Tableau, Power BI, matplotlib, or Plotly.
• Working knowledge of cloud platforms (AWS, GCP, or Azure).
• Experience using Git for version control and collaborative development.
• Exposure to MLOps practices and tools (MLflow, Kubeflow, Docker, CI/CD pipelines).
  
  

• Experience in NLP, time-series forecasting, or recommendation systems.
• Familiarity with big data technologies such as Spark, Hive, or Presto
  
  

(ref:hirist.tech)