About the Role
We are seeking a Senior Data Scientist to design, build, and scale advanced machine learning solutions that power customer engagement, optimization, and decision-making systems. This role is ideal for someone who enjoys translating complex business challenges into robust, production-ready data products and thrives at the intersection of machine learning, software engineering, and data platform development.
You will take deep ownership of the end-to-end ML lifecycle, from model design and experimentation to deployment, monitoring, and continuous improvement. Beyond model development, you will play a critical role in building reliable data foundations, ensuring engineering excellence, and driving scalable solutions that deliver measurable business impact.
Key Responsibilities
Develop Advanced Machine Learning Solutions
- Design, train, evaluate, and deploy machine learning models to solve business problems across customer behavior, prediction, segmentation, recommendation, optimization, forecasting, and related domains.
- Translate business requirements into practical and scalable data science solutions.
- Select appropriate algorithms, features, and evaluation methodologies to maximize model performance and business value.
Build and Maintain Data & ML Infrastructure
- Architect and develop scalable data pipelines supporting both structured and unstructured data workloads.
- Design data models, schemas, and transformation frameworks that enable reliable machine learning operations.
- Integrate new data sources, manage backfills, and optimize pipeline performance, scalability, and cost efficiency.
Ensure Model Reliability and Production Readiness
- Establish rigorous validation, monitoring, and quality-control processes throughout the model lifecycle.
- Detect and mitigate issues such as data quality degradation, drift, bias, and operational edge cases.
- Implement automated testing and governance practices to maintain high standards of reliability and reproducibility.
Drive Engineering Excellence
- Write clean, maintainable, and production-grade Python and SQL code.
- Apply software engineering best practices including code reviews, testing, documentation, and CI/CD automation.
- Continuously improve platform health by reducing technical debt, streamlining workflows, and decommissioning obsolete assets.
Qualifications
- Bachelor's degree or higher in Computer Science, Statistics, Mathematics, Physics, Engineering, Data Science, or a related quantitative discipline.
- At least 4 to 6 years of hands-on experience building and deploying machine learning solutions in production environments.
- Strong proficiency in Python and SQL, with experience developing scalable data pipelines and data processing frameworks.
- Demonstrated experience working with large datasets and end-to-end machine learning workflows, from experimentation through deployment and monitoring.
- Strong understanding of statistical modeling, machine learning techniques, model validation methodologies, and data quality controls.
- Experience applying data science to customer analytics, personalization, recommendation systems, lifecycle management, growth optimization, or similar business domains is highly desirable.
- Strong problem-solving skills and the ability to operate independently in a fast-paced, highly technical environment.