Data Scientist

The Data Scientist solves complex analytical and business problems using quantitative approaches through a combination of analytical, mathematical, and technical skills. This role researches, designs, implements, and validates advanced algorithms and machine learning models to analyze diverse data sources and generate actionable insights. The Data Scientist leverages statistical analysis, predictive modeling, machine learning, and Generative AI (GenAI) techniques to drive business outcomes, optimize processes, and enable data-driven decision-making across the organization. Key Responsibilities • Leverage data science methodologies to solve complex business challenges and deliver measurable business impact. • Research, design, develop, and deploy machine learning, deep learning, predictive analytics, and Generative AI solutions, including Large Language Model (LLM)-based use cases. • Build analytical models using statistical methodologies and programming tools to generate insights and recommendations. • Perform data extraction, cleaning, preparation, profiling, and transformation to enable high-quality analysis and model development. • Conduct feature engineering, model experimentation, hyperparameter tuning, and lifecycle optimization to improve model performance and scalability. • Develop descriptive, predictive, and prescriptive models for regression, classification, anomaly detection, forecasting, and pattern recognition. • Partner with business stakeholders, product teams, and domain experts to understand requirements and validate model outcomes. • Implement model deployment and production pipelines using APIs, containerization, and cloud-based ML platforms. • Monitor model performance, accuracy, and drift; recommend improvements and retraining strategies as needed. • Communicate methodologies, findings, insights, and recommendations clearly to technical and non-technical stakeholders. • Stay current with emerging technologies, trends, and best practices in data science, AI, machine learning, and cloud computing. • Contribute to MLOps practices including CI/CD, testing, automation, and governance to ensure reliable production deployments.