Senior Manager, Data Science & Analytics

Role Overview: You will be responsible for providing enterprise-wide strategic leadership in Clean Energy analytics. This involves defining how advanced data science, AI, and decision intelligence can drive long-term business transformation and sustainability outcomes. As the principal authority on data science strategy, you will ensure alignment with corporate goals, regulatory requirements, and clean energy transition objectives. Your role will involve leading and mentoring multiple data science teams or domain leads to consistently deliver high-impact, scalable analytics solutions across business units. Additionally, you will partner with CXOs, executive leadership, and functional heads to shape analytics-led decisions, risk posture, and capital optimization strategies. You will also be tasked with architecting, governing, and overseeing the end-to-end lifecycle of advanced analytics, ML, and AI solutions by leveraging large-scale structured and unstructured data from various systems. Key Responsibilities: - Define and monitor enterprise-level KPIs and value realization frameworks to measure the business impact of analytics initiatives across safety, environment, production, and cost. - Establish advanced risk intelligence frameworks for operational, safety, environmental, and regulatory risk quantification using AI/ML. - Drive organization-wide data science governance, standards, and best practices, including model lifecycle management, ethics, explainability, and compliance. - Own the multi-year data science and AI roadmap, aligning technology investments with business strategy in collaboration with Digital, IT, and Cybersecurity leadership. - Lead enterprise-scale AI initiatives such as predictive maintenance platforms, real-time anomaly detection systems, optimization engines, and forecasting solutions. - Guide the formulation of next-generation algorithms and AI products supporting clean energy transition, decarbonization, and digital innovation. - Define the scope, design, and implementation of ML/AI models to support initiatives such as predictive maintenance, anomaly detection, optimization, and forecasting. - Lead the formulation and optimization of algorithms supporting clean energy products, services, and stakeholder outcomes. - Collaborate with digital leaders to ensure secure, reliable, scalable, and cost-effective analytics infrastructure and operations. - Ensure effective communication with internal teams, leadership, and stakeholders at all levels. Qualifications Required: - Expert-level proficiency in SQL, Python, and enterprise-scale analytics development. - Deep mastery of advanced statistics, machine learning, AI, optimization, and experimental design, with the ability to guide methodological choices. - Proven expertise in big data and distributed computing architectures (Hadoop ecosystem, cloud-native analytics platforms). - Hands-on experience with statistical and modeling tools such as SAS, R, MATLAB, Statistical and advanced visualization platforms. - Advanced knowledge of time-series modeling, anomaly detection, and industrial sensor analytics in clean energy or large-scale operational environments. Strong understanding of data management, databases (SQL, PL/SQL), and data pipelines. - Exceptional executive communication, storytelling, and influence skills. (Note: No additional details of the company were provided in the job description) Role Overview: You will be responsible for providing enterprise-wide strategic leadership in Clean Energy analytics. This involves defining how advanced data science, AI, and decision intelligence can drive long-term business transformation and sustainability outcomes. As the principal authority on data science strategy, you will ensure alignment with corporate goals, regulatory requirements, and clean energy transition objectives. Your role will involve leading and mentoring multiple data science teams or domain leads to consistently deliver high-impact, scalable analytics solutions across business units. Additionally, you will partner with CXOs, executive leadership, and functional heads to shape analytics-led decisions, risk posture, and capital optimization strategies. You will also be tasked with architecting, governing, and overseeing the end-to-end lifecycle of advanced analytics, ML, and AI solutions by leveraging large-scale structured and unstructured data from various systems. Key Responsibilities: - Define and monitor enterprise-level KPIs and value realization frameworks to measure the business impact of analytics initiatives across safety, environment, production, and cost. - Establish advanced risk intelligence frameworks for operational, safety, environmental, and regulatory risk quantification using AI/ML. - Drive organization-wide data science governance, standards, and best practices, including model lifecycle management, ethics, explainability, and compliance. - Own the multi-year data science and AI roadmap, aligning tech