Data Scientist

Role purpose: Apply statistical analysis, machine learning, and data engineering practices to generate actionable insights, build predictive models, and support data-driven decision-making across the organization. Core responsibilities - Collect, clean, and transform structured and unstructured data from multiple sources. - Design, train, evaluate, and deploy machine learning models for forecasting, classification, recommendation, or anomaly detection. - Develop reproducible analytics workflows, experiments, and A/B tests to measure impact. - Build dashboards, reports, and data visualizations to communicate findings to technical and non-technical stakeholders. - Collaborate with product, engineering, and business teams to define metrics, success criteria, and data requirements. - Monitor model performance, data quality, and drift; iterate to improve accuracy and reliability. - Document methods, assumptions, and results to ensure transparency and governance. Required skills -

Programming: Python and/or R; SQL for querying and data manipulation. - Machine learning: Supervised/unsupervised methods, feature engineering, model evaluation, and tuning. - Statistics: Hypothesis testing, regression, probability, experimental design. - Data tools: ETL/ELT concepts, notebooks, version control, and cloud data platforms. - Communication: Translate complex analyses into clear recommendations and measurable outcomes. Success measures - Improved business KPIs through deployed models and insights. - Reliable, well-documented pipelines an...