Data Scientist

Role & responsibilities Designed and developed predictive and prescriptive analytics models to support data-driven business decisions and strategic initiatives. Gathered, analyzed, and interpreted complex business requirements to identify opportunities for analytics, automation, and process optimization. Explored and analyzed structured and unstructured data from multiple sources to identify trends, patterns, and actionable business insights. Collaborated with business stakeholders to define analytical approaches and translate business challenges into machine learning and statistical solutions. Conducted exploratory data analysis (EDA), data profiling, and hypothesis testing to validate business assumptions and identify improvement opportunities. Designed data preparation pipelines including data cleansing, transformation, standardization, feature engineering, and dimensionality reduction. Built, trained, evaluated, and optimized statistical and machine learning models using appropriate algorithms based on business objectives. Compared multiple models using performance metrics and selected the most effective solution while clearly communicating model rationale to business stakeholders. Developed scalable and efficient analytical solutions for large datasets using Python, SQL, and machine learning techniques. Interpreted model outputs and translated analytical findings into meaningful business recommendations for technical and non-technical audiences. Created dashboards, reports, and visualizations to communicate KPIs, trends, and predictive insights to senior leadership. Assisted business users in understanding model predictions, outputs, and decision-making frameworks. Monitored model performance, tracked prediction accuracy, and recommended improvements to maintain model effectiveness over time. Worked closely with cross-functional teams including Product, Business, Technology, and Operations to deliver analytics-driven solutions. Identified opportunities to improve operational efficiency through process automation and advanced analytics. Recommended data collection strategies and experimental design methodologies to improve model quality and business outcomes. Ensured analytical solutions aligned with business objectives, regulatory requirements, and organizational standards. Managed stakeholder expectations through regular communication, project updates, and successful delivery against agreed service levels. Built strong relationships with internal and external stakeholders while providing subject matter expertise on analytics and data science initiatives. Contributed to continuous improvement initiatives by identifying process gaps, recommending best practices, and driving innovation across the organization. Delivered high-quality analytical solutions within project timelines while maintaining accuracy, scalability, and business value. Preferred candidate profile