Job Purpose

Responsible for owning and delivering complex data science solutions end-to-end, leveraging advanced modeling, experimentation, and analytics to drive business impact and data-driven decision-making. Ensures scalable and business-aligned outcomes, collaborates with cross-functional teams, and mentors junior data scientists to enhance team capability and performance.

Key Result Responsibilities

• Translate complex business challenges into scalable data science and machine learning solutions that deliver measurable business value. 
• Own the end-to-end data science lifecycle, including problem definition, data exploration, experimentation, model development, validation, deployment, and monitoring. 
• Design and implement advanced analytical, predictive, and machine learning models to address business requirements. 
• Develop and optimize features, algorithms, and model performance to improve accuracy, efficiency, and business impact. 
• Ensure solutions are scalable, reliable, and aligned with business objectives, governance standards, and best practices. 

Key Result Responsibilities-Continued

• Collaborate with Data Engineering teams to define data requirements, improve data quality, and ensure the availability of reliable data assets. 
• Partner with ML Engineering and Platform teams to support model deployment, integration, monitoring, and production performance. 
• Mentor junior data scientists through technical guidance, code reviews, and knowledge sharing. 
• Communicate insights and recommendations to stakeholders, enabling data-driven decision-making across the organization.

Qualifications (Academic, training, languages)

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, Mathematics, Engineering, or a related field (or equivalent practical experience). 
• Strong expertise in machine learning and statistical modeling
• Advanced experience in experimentation (A/B testing, hypothesis testing)
• Ability to work with large and complex datasets
• Strong coding and model optimization capabilities
• Strong understanding of production constraints and model lifecycle
• Familiarity with monitoring concepts and model performance tracking
• Ability to define data and deployment requirements and effectively coordinate with Data Engineering and ML Platform teams

Work Experience

• With 4-6 years of experience in data science or machine learning roles
• Experience selecting and designing appropriate modeling approaches
• Experience working with deployed models and iterative improvements