What we're looking for :

- 10+ years of experience in a data scientist, ML engineer, or advanced analytics role.

- Strong foundation in statistics - hypothesis testing, regression, time series analysis, Bayesian methods.

- Advanced SQL - comfortable writing complex queries across large, multi-source datasets.

- Proficiency in Python or R for analysis, modeling, and automation.

- Experience with ML/statistical libraries (scikit-learn, statsmodels, pandas, NumPy, or similar).

- Experience with AWS data and ML services (SageMaker, Redshift, Athena, Glue, QuickSight, or similar).

- Hands-on experience with Tableau.

- Demonstrated ability to define metrics frameworks and build dashboards from scratch, not just maintain existing ones.

- Experience building anomaly detection or predictive models in a production or operational context.

Key Responsibilities :

- Architect and implement end-to-end machine learning pipelines to solve complex business problems, ensuring high model accuracy and production-grade reliability.

- Lead the development of predictive analytics models that provide actionable insights for stakeholders, directly impacting service delivery and operational performance.

- Design and execute advanced data mining and modeling strategies to extract meaningful patterns from large, unstructured datasets.

- Collaborate with engineering teams to integrate data-driven features into existing product ecosystems, enhancing the overall value proposition for our clients.

- Mentor junior data scientists and foster a culture of technical rigor, ensuring best practices in code quality, documentation, and model validation are maintained.