Sr Data Scientist

Primary Purpose

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. 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. Become a subject matter expert in the areas of artificial intelligence, machine learning, feature engineering, data mining, and data manipulation/storage. Demonstrate commitment to continuous learning and professional development in technical subject matter.

Share knowledge with team members, and business stakeholders, and IT partners. Collect, cleanse, standardize and analyze data from a variety of internal and external sources. Produce novel insights to help inform business actions using statistical modeling and machine learning techniques on complex data‑sets on the order of several terabytes or petabytes. Position may be required to work extended hours for coverage during storms or other energy delivery emergencies.

• Develop key predictive models that lead to delivering a premier customer experience, operating performance improvement, and increased safety best practices. Develop and recommend data sampling techniques, data collections, and data cleaning specifications and approaches. Apply missing data treatments as needed. (25%)
• Analyze data using advanced analytics techniques in support of process improvement efforts using modern analytics frameworks, including but not limited to Python; SQL;
  Spark, Hadoop file system and others (15%)
• Access and analyze data sourced from various Company systems of record. Support the development of strategic business, marketing, and program implementation plans. (15%)
• Access and enrich data warehouses across multiple Company departments. Build, modify, monitor and maintain high-performance computing systems. (5%)
• Provide expert data and analytics support to multiple business units (20%)
• 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 Exelon on big data analytics techniques and tools to improve analytical capabilities. (20%)

• 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 organization’s advance analytics capabilities through internal presentations, training workshops, and publications.

• Education:
  
  Bachelor's degree in a Quantitative discipline. Ex:
  Applied Mathematics, Computer Science, Finance, Operations Research, Physics, Statistics, or related field
• 4‑7 years of relevant experience developing hypotheses, applying machine learning algorithms, validating results to analyze multi‑terabyte datasets and extracting actionable insights is required. Previous research or professional experience applying advanced analytic techniques to large, complex datasets.
• Analytical Abilities:
  Strong knowledge in at least two of the following areas: machine learning, artificial intelligence, statistical modeling, data mining, information retrieval, or data visualization.
• Technical…