Data Scientist​/Data Scientist, Senior

Data Scientist / Data Scientist, Senior

Are you a Data Scientist / Data Scientist, Senior ready to make a big impact at scale? We’re looking for a highly skilled Data Scientist / Data Scientist, Senior to lead the design and deployment of production‑grade machine‑learning systems in a complex enterprise environment. You’ll own the full MLOps lifecycle—from prototyping to monitoring—and architect solutions that power intelligent, real‑time decision‑making across critical business functions.

This is a high‑visibility role where you’ll collaborate with cross‑functional teams, influence architecture, and help define best practices that shape the future of ML at scale.

• Lead MLOps initiatives: design, build, deploy, and monitor end‑to‑end ML solutions that are scalable, reliable, and secure.
• Architect for scale & speed: build applications optimized for low latency on high‑volume data pipelines and streaming environments.
• Advise & innovate: act as a thought partner to data scientists and engineering leaders, bringing deep domain expertise in ML model design and infrastructure.
• Collaborate cross‑functionally: work with enterprise architects, product teams, and data scientists to deliver real‑world business value.
• Own quality & governance: establish and maintain best practices for ML lifecycle management, including CI/CD, monitoring, testing, and documentation.

• Data Scientist
• BS degree in Data Science, Computer Science, Information Sciences, Mathematics, Engineering or related field.
• Minimum four (4) years directly related data analytics, data science, predictive modeling, machine learning, statistical modeling and/or user experience role.
• OR advanced degree and two (2) years directly related experience.
• Strong analytical and problem‑solving skills and programming knowledge, or an equivalent combination of education and experience.
• Data Scientist, Senior
• BS degree in Data Science, Computer Science, Information Sciences, Mathematics, Engineering or related field.
• Minimum six (6) years directly related data analytics, data science, predictive modeling, machine learning, statistical modeling and/or user experience role.
• OR advanced degree and four (4) years directly related experience.
• Strong analytical, problem‑solving, and programming skills, or an equivalent combination of education and experience.
• Preferred Special Skills, Knowledge or Qualifications
• Master’s or Doctorate degrees in related fields.
• Knowledge/experience in utility industry and business functions.
• Certification in Data Science and/or predictive analytics.
• High proficiency in R, Python, SQL and related tools.
• Strong communication, presentation and writing skills.
• Ability to lead teams in evaluations and implementation of solutions and work with stakeholders at all levels.

• 1) Collaboration with customers and partners:

  Consult with stakeholders and subject matter experts to understand business needs, operations, goals, objectives, and key performance drivers. Work closely with business units to complete data analytics efforts. Build and maintain strong working relationships with customers, partners, and vendors.

• 2) Data requirements and preparation:

  Identify available and relevant data and sources. Collaborate with SMEs, data stewards and architects for data collection, preparation, integration, quality, exploration and retention.

  • Gather data, formulate clusters or nodes and establish performance checks on large data models.
  • Design and implement solutions including data acquisition, storage, transformation and analysis.
• 3) Modeling and deployment:
  • Design, develop and deploy innovative models. Provide insights from predictive statistical modeling activities.
  • Develop models, algorithms and visualizations to distill insights from vast volumes of data.
  • Use statistical, algorithmic, mining and visualization techniques to discover insights and opportunities.
  • Turn data into critical information and knowledge for organizational decisions.
  • Propose innovative approaches and validate findings through experimental and iterative methods.
  • Maintain knowledge of data transformation platforms and technologies.
• 4) Present results, provide recommendations and lead analytics efforts:
  • Present findings in an accessible manner to business stakeholders.
  • Make recommendations based on business requirements and industry best practices.
  • Make technical decisions on advanced analytics initiatives.
• 5) Programming and coding:
  • Utilize R, Python, SQL, .NET, Java or C++ to extract data from sources and model.
  • Familiarity with cloud architecture and building using cloud technologies.
  • Perform data acquisition using JSON, SQL, ODBC, JScript, or API for Big Data extracts.
  • Transform and utilize streaming data with Kafka, SQL, Spark, and/or Azure.
• 6) Mentoring and coaching junior staff as necessary.