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Data Scientist, IT
Constellation Energy
- United States
- United States
About
Apply the appropriate data science or analytical methods to extract knowledge and insights from data, which may take the form of time-series, structured (relational data stores), and unstructured (text and multi-media) data sets. Closely collaborate with various internal stakeholders, information architects, data engineers, project/program managers, and other teams to turn data into critical information to inform decision making. This requires understanding business needs, providing and receiving regular feedback, and planning the proper transfer of developed solutions. Mine big and small data for insights, using advanced statistic and machine learning methods. Validate findings with the business by sharing analysis outputs in a way that can be understood by business stakeholders. Demonstrate commitment to continuous learning and professional development in technical subject matter. Primary Duties and Accountabilities: Develop key predictive models that lead to delivering reduced overall annual expense for nuclear, performance improvement, and optimize specific performance criteria. 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 Access and analyze data sourced from various Company systems of record. Support the development of strategic business and program implementation plans. Access and enrich data warehouses across multiple Company departments. Build, modify, monitor and maintain high-performance computing systems. Works with stakeholders and subject matter experts to understand business needs, goals and objectives. Work closely with business, engineering, and technology teams to develop solution to data-intensive business problems and translates them into data science projects. Collaborate with other analytic teams across Constellation on big data analytics techniques and tools to improve analytical capabilities. Minimum Qualifications: Bachelor's degree in a quantitative discipline such as Data Science, Data Analytics, Applied Mathematics, Statistics, Computer Science, Operations Research, or a related field and 2-years of relevant experience. OR Master's Degree in quantitative discipline Relevant experience developing hypotheses, applying machine learning algorithms, validating results, and analyzing large datasets to extract actionable insights, including prior research or professional experience using advanced analytic techniques on large and complex datasets Knowledge in at least two of the following areas: machine learning, artificial intelligence, statistical modeling, data mining, information retrieval, or data visualization Technical Knowledge: Proven experience in developing and deploying predictive analytics projects using one or more leading languages (Python, R, Scala, etc.). Strong communication skills, both written and oral Demonstrated ability to translate data analysis into clear conclusions and actionable recommendations for business partners, practice leaders, and executives Preferred Qualifications: Strong analytical skills with a solid understanding of machine learning, statistics, probability theory, data structures and algorithms, and optimization Technical Knowledge: Expert level coding skills (Python, R, Scala, SQL, etc.) Proficiency in database management and large datasets: create, edit, update, join, append and query data from columnar and big data platforms Excellent communication and negotiation skills Demonstrated ability to meet critical deadlines and take accountability for task completion Knowledge in at least two of the following areas: machine learning, artificial intelligence, statistical modeling, data mining, natural language processing, or data visualization. Foundational understanding of LLM concepts, prompt engineering, and AI application development.
Languages
- English
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