Data Scientist role

Responsibilities:
Problem Definition & Hypothesis Generation: Work closely with business stakeholders, product managers, and engineers to understand key business challenges and formulate data-driven hypotheses.Data Collection & Preparation: Identify, collect, and prepare large, complex datasets from various sources. This includes data cleaning, transformation, feature engineering, and ensuring data quality and integrity.Exploratory Data Analysis (EDA): Perform in-depth statistical analysis and visualization to uncover trends, patterns, outliers, and relationships within data, generating actionable insights.Model Development & Evaluation:Design, develop, and implement machine learning models (e.g., supervised, unsupervised, reinforcement learning) for various applications such as prediction, classification, recommendation, anomaly detection, and natural language processing.Select appropriate algorithms, fine-tune model parameters, and perform rigorous model validation and evaluation (e.g., cross-validation, A/B testing).Monitor model performance post-deployment and iterate for continuous improvement.Statistical Analysis & Experimentation: Apply advanced statistical techniques to analyze experimental results, identify causal relationships, and provide recommendations based on data-driven evidence.Insight Generation & Communication: Translate complex analytical findings and model results into clear, concise, and actionable insights for non-technical audiences through presentations, reports, and dashboards.Deployment & MLOps: Collaborate with MLOps engineers and software developers to productionize models, ensure scalability, reliability, and maintainability.Research & Innovation: Stay updated with the latest advancements in data science, machine learning, and AI, and proactively identify opportunities to apply new techniques to solve business problems.Documentation: Document code, models, methodologies, and findings thoroughly for reproducibility and knowledge sharing.