Lead Data Scientist - Machine Learning

As a Lead Data Scientist at our company, you will be responsible for driving the design, development, and deployment of advanced analytics, machine learning, and predictive modeling solutions. Your expertise in Python, Spark, big data technologies, and statistical modeling will be crucial in leading data science initiatives and mentoring team members. You will play a key role in translating complex business problems into scalable data-driven solutions that deliver measurable business impact. Key Responsibilities: - Lead end-to-end development and deployment of machine learning and predictive analytics solutions. - Design and implement scalable data science frameworks, models, and algorithms for business-critical applications. - Analyze large and complex datasets to identify trends, patterns, and actionable insights. - Build and optimize machine learning models for forecasting, classification, recommendation, segmentation, and anomaly detection. - Develop data pipelines and analytical workflows using Python, Spark, and big data technologies. - Collaborate with business stakeholders to understand requirements and translate them into analytical solutions. - Drive model validation, performance monitoring, and continuous improvement initiatives. - Lead proof-of-concepts and innovation projects leveraging advanced AI and machine learning techniques. - Mentor and guide junior data scientists, analysts, and engineers. - Work closely with data engineering teams to ensure data quality, governance, and scalability. - Present analytical findings and recommendations to senior leadership and business teams. Required Technical Skills: - Strong programming expertise in Python. - Hands-on experience with Apache Spark and distributed computing frameworks. - Expertise in Predictive Analytics and Machine Learning algorithms. - Strong knowledge of statistics, probability, and data modeling techniques. - Experience working with large-scale structured and unstructured datasets. - Proficiency in SQL and data manipulation techniques. - Experience with Hadoop ecosystem tools including Hive and MapReduce. - Knowledge of feature engineering, model tuning, and model evaluation methodologies. - Experience in data visualization and storytelling using analytical tools. - Understanding of MLOps concepts, model deployment, and monitoring. - Experience with cloud-based analytics platforms is preferred. - Knowledge of optimization tools such as Gurobi will be an added advantage. Preferred Skills: - Strong problem-solving and analytical thinking capabilities. - Experience leading data science teams and projects. - Excellent stakeholder management and communication skills. - Ability to translate business challenges into analytical solutions. - Strong understanding of data architecture and big data ecosystems. - Experience in Agile development environments. - Passion for innovation and continuous learning in AI and Data Science. Qualifications: - Bachelor's Degree in Engineering, Computer Science, Mathematics, Statistics, or a related discipline. - 7+ years of experience in Data Science, Machine Learning, Predictive Analytics, and Big Data technologies. - Proven track record of delivering enterprise-scale analytics and machine learning solutions. - Experience in leading cross-functional teams and stakeholder engagements. As a Lead Data Scientist at our company, you will be responsible for driving the design, development, and deployment of advanced analytics, machine learning, and predictive modeling solutions. Your expertise in Python, Spark, big data technologies, and statistical modeling will be crucial in leading data science initiatives and mentoring team members. You will play a key role in translating complex business problems into scalable data-driven solutions that deliver measurable business impact. Key Responsibilities: - Lead end-to-end development and deployment of machine learning and predictive analytics solutions. - Design and implement scalable data science frameworks, models, and algorithms for business-critical applications. - Analyze large and complex datasets to identify trends, patterns, and actionable insights. - Build and optimize machine learning models for forecasting, classification, recommendation, segmentation, and anomaly detection. - Develop data pipelines and analytical workflows using Python, Spark, and big data technologies. - Collaborate with business stakeholders to understand requirements and translate them into analytical solutions. - Drive model validation, performance monitoring, and continuous improvement initiatives. - Lead proof-of-concepts and innovation projects leveraging advanced AI and machine learning techniques. - Mentor and guide junior data scientists, analysts, and engineers. - Work closely with data engineering teams to ensure data quality, governance, and scalability. - Present analytical findings and recommendations to senior leadership and business teams. **Require