Senior Data scientist

Role & responsibilities

Lead end-to-end data science initiatives including data acquisition, exploratory analysis, feature engineering, model development, validation, deployment, monitoring, and optimization activities; design, develop, and deploy predictive models, forecasting algorithms, machine learning frameworks, and statistical models in production environments; collaborate with business stakeholders, product teams, and engineering teams to define analytical strategies, business requirements, and data-driven solution approaches; identify, analyze, and utilize structured and unstructured data sources to support enterprise analytics and operational initiatives; perform data wrangling, preprocessing, cleansing, transformation, and feature engineering activities to improve model performance and analytical accuracy; develop scalable machine learning and optimization solutions using Python, R, SQL, and data science frameworks including scikit-learn, TensorFlow, PyTorch, XGBoost, and Statsmodels; support big data processing and distributed computing initiatives using Hadoop, Spark, Hive, and related technologies; create dashboards, visualizations, and analytical reporting solutions using Tableau, Power BI, Plotly, Dash, and other visualization tools; implement MLOps practices including model versioning, monitoring, retraining, explainability, deployment workflows, and operational lifecycle management; collaborate with cross-functional engineering teams to support data product development and production deployment activities; analyze business challenges and provide actionable insights, recommendations, and optimization strategies to leadership stakeholders; mentor junior data scientists and contribute to data science best practices, knowledge sharing, process standardization, and continuous improvement initiatives; support data governance, privacy, security, and ethical AI/ML compliance requirements across enterprise analytics environments.