Data Scientist

About

The Role
The role owns the design, development, and deployment of predictive modeling systems that drive critical business decisions across high-scale applications. The team collaborates closely with data engineers and product stakeholders to translate ambiguous problems into robust statistical frameworks and scalable machine learning solutions.


Key Responsibilities

• Design and develop scalable machine learning models, statistical algorithms, and predictive analytics pipelines using Python, SQL, and Py Spark
• Partner with data engineering teams to build clean feature stores and robust data pipelines that ensure model integrity across environments
• Deploy, monitor, and maintain production models using cloud infrastructure and MLOps tools like MLflow, AWS, or GCP
• Conduct rigorous A/B testing and experimentation analysis to quantify the business impact of deployed algorithms
• Communicate complex technical findings and model behaviors clearly to non-technical stakeholders and leadership teams
• Write maintainable, well-tested code and contribute to the ongoing refinement of internal data science standards

What We Are Looking For

• 3–6 years of experience in data science, quantitative modeling, or machine learning engineering with production-grade deliverables
• Proficiency in Python and standard data science libraries:
  Pandas, Num Py, Scikit-Learn, PyTorch or Tensor Flow
• Strong foundation in statistical analysis, experimental design, hypothesis testing, and advanced regression techniques
• Hands-on experience with SQL and distributed data processing frameworks like Spark or Big Query
• BS or MS in Computer Science, Statistics, Mathematics, Economics, or a related quantitative field
• Bonus:
  Experience deploying models via containerization (Docker/Kubernetes) or working with LLM and GenAI architectures