PRIMARY PURPOSE OF POSITION - Apply the scientific method to extract knowledge and insights from data, which may take the form of time-series (smart-meters, smart-grid, and other IoT), structured (relational data stores), and unstructured (text and multi-media) data sets. Train state of the art algorithmic models, including but not limited to tree-based approaches and neural networks, and implement those models into a production environment following the established ML - Ops approaches. Closely collaborate with various internal stakeholders, information architects, data engineers, project/program managers, and other teams to turn data into analytics-driven products and inform decision making. This requires understanding business needs, providing and receiving regular feedback, and planning the proper transfer of developed solutions. Validate findings with the business by sharing analysis outputs in a way that can be understood by business stakeholders. Become a subject matter expert in the areas of artificial intelligence, machine learning, feature engineering, and high-performance computing. Demonstrate commitment to continuous learning and professional development in technical subject matter. Share knowledge with team members, and business stakeholders, and IT partners. A successful candidate will quickly adopt the team's established working processes and toolkit while growing his/her knowledge of the utilities industry. Position may be required to work extended hours for coverage during storms or other energy delivery emergencies. Primary Duties. PRIMARY DUTIES AND ACCOUNTABILITIES - Develop key predictive models that lead to delivering a premier customer experience, operating performance improvement, and increased safety best practices. (25%)Analyze data using advanced analytics techniques in support of process improvement efforts using modern analytics frameworks, including but not limited to Python, R, Scala, or equivalent; Spark, Hadoop file system and others (20%)Access and analyze data sourced from various Company systems of record. Support the development of strategic business, marketing, and program implementation plans. (20%)Provide expert data and analytics support to multiple business units (20%)Access and enrich data warehouses across multiple Company departments. Build, modify, monitor and maintain high-performance computing systems. (15%) Job Scope. JOB SCOPE - Support business unit strategic planning while providing a strategic view on machine learning technologies. Advice and counsel key stakeholders on machine learning findings and recommend courses of action that redirect resources to improve operational performance or assist with overall emerging business issues. Provide key stakeholders with machine learning analyses that best positions the company going forward. Educate key stakeholders on the organizations advance analytics capabilities through internal presentations, training workshops, and publications. Minimum Qualifications. MINIMUM QUALIFICATIONS - Bachelor's or Master's degree from a leading program in a Quantitative discipline. Ex: Applied Mathematics, Computer Science, Finance, Operations Research, Physics, Statistics, or related field. Intern experience in a data science position or previous research or professional experience applying advanced analytic techniques to large, complex datasets. Strong knowledge in at least two of the following areas: machine learning, artificial intelligence, statistical modeling, data mining, information retrieval, or data visualization. Demonstratable experience in your analytics/statistics/visualization platform of choice, but preferably in the MS Azure suite as well as Python, SQL. using big data technologies like Spark, Dask, etc. Ability to translate data analysis and findings into coherent conclusions and actionable recommendations to business partners, practice leaders, and executives. Strong oral and written communication skills. Preferred Qualifications. PREFERRED QUALIFICATIONS - Master's from a leading program in a Quantitative discipline. Prior exposure to data structures pertaining to smart-meters, billing, or outage management systems. Prior exposure to the utilities or broader energy sector. Solid understanding of relevant theories in machine learning, statistics, probability theory, data structures and algorithms, optimization, etc. Expert level coding skills (Python, R, Scala, etc), and experience developing in a Unix environment. Ability to translate executive and analytics leaders vision and guidance into methods and analytics. Strong time management and presentation skills. Experience presenting to diverse audiences including presenting to conferences and business symposia. Benefits.