Data Scientist

About the Role We are looking for an experienced Data Scientist to lead the design and development of data-driven solutions that enable smarter business decisions and predictive capabilities. The ideal candidate combines strong analytical skills, statistical expertise, and hands-on experience with machine learning and big data technologies. You will work collaboratively with data engineers, analysts, and business stakeholders to deliver insights and scalable ML models that create measurable impact. Key Responsibilities Data Exploration & Analysis Collect, clean, and analyze structured and unstructured data from multiple sources to uncover meaningful insights and trends. Model Development Design, build, and deploy machine learning and statistical models to solve business problems such as forecasting, classification, recommendation, and optimization. Feature Engineering Identify, create, and select the most relevant variables and features to improve model performance and interpretability. Experimentation & Validation Apply hypothesis testing, A/B testing, and cross-validation techniques to evaluate model robustness and performance. Production Deployment Work with data engineering and MLOps teams to operationalize models, monitor performance, and ensure scalability and reliability in production environments. Visualization & Storytelling Communicate complex analytical findings in clear, concise, and visually compelling ways for both technical and non-technical audiences. Collaboration Partner with business teams to understand objectives, define success metrics, and translate business requirements into analytical frameworks. Continuous Improvement Stay current with advances in machine learning, AI, and data science technologies, incorporating them into projects and best practices. Technical Expertise Strong proficiency in Python (NumPy, Pandas, Scikit-learn, PyTorch, TensorFlow) or R. Expertise in machine learning algorithms (supervised, unsupervised, NLP, and deep learning). Strong understanding of statistical modeling, probability, and mathematical optimization. Experience with SQL and data manipulation in large datasets. Familiarity with big data platforms (e.g., Spark, Databricks, Hadoop) and cloud environments (AWS, Azure, or GCP). Exposure to MLOps tools (MLflow, Kubeflow, Airflow, Docker, CI/CD). Experience with data visualization tools (Power BI, Tableau, Matplotlib, Seaborn, Plotly).7-10 yrs experience