Desired Qualifications: A Data Scientist with 10 to 15 years of experience plays a pivotal role in an organization, harnessing advanced analytics, machine learning, and data-driven insights to guide critical business decisions. This role requires deep expertise in data science, a proven track record of successfully implementing data solutions, and strong leadership capabilities. Key Responsibilities: Data Analysis: Expertly handle complex data sets, conduct in-depth data analysis, and derive actionable insights by applying advanced statistical and machine learning techniques. Predictive Modeling: Develop and deploy sophisticated machine learning models, utilizing algorithms like deep learning, ensemble methods, and neural networks to predict trends, behaviors, and outcomes. Data Visualization: Create compelling data visualizations that effectively communicate complex findings and insights using tools like Tableau, Power BI, or custom Python visualizations. Feature Engineering: Lead feature engineering efforts to identify and select critical data features, enhancing the predictive power of machine learning models. Statistical Validation: Formulate, implement, and test hypotheses, providing robust statistical validation for key business decisions. Algorithm Development: Lead the development of machine learning algorithms and their optimization to solve complex business problems. Data Integration: Collaborate with IT and data engineering teams to integrate and access data from various sources, data lakes, and data warehouses. Model Deployment: Oversee the deployment of machine learning models in production environments to support real-time decision-making and business applications. Experimentation & A/B Testing: Design and analyze A/B tests to measure the impact of changes, optimizations, and improvements. Data Ethics: Ensure ethical data practices, privacy compliance, and adherence to data protection regulations in all data science initiatives. Cross-functional Collaboration: Collaborate closely with cross-functional teams, including engineers, business analysts, domain experts, and executives to understand business requirements and align data science initiatives with organizational goals. Mentorship: Provide mentorship and guidance to junior data scientists, fostering their growth and development. Strategic Leadership: Act as a strategic leader, influencing data-driven culture across the organization, defining the data science roadmap, and contributing to long-term data strategy. Innovation: Stay updated on the latest data science tools, techniques, and trends, continuously innovating and evaluating new technologies to improve data science practices. • Qualifications: Master's or Ph.D. in a quantitative field (e.g., Computer Science, Statistics, Mathematics, Engineering). 10 to 15 years of experience in data science, including an extensive track record of implementing data solutions and driving data-driven decision-making. Proficiency in data analysis tools and programming languages such as Python, R, or Julia. Expert knowledge of machine learning algorithms and their applications. Exceptional skills in data visualization tools like Tableau, Power BI, or data visualization libraries in Python (e.g., Matplotlib, Seaborn). Profound understanding of databases and data manipulation using SQL. Outstanding problem-solving and critical thinking abilities. Strong leadership and communication skills, capable of conveying complex findings and insights to both technical and non-technical stakeholders. Extensive experience with big data technologies and distributed computing frameworks (e.g., Hadoop, Spark). Expertise in data ethics, privacy, and compliance considerations.