Sr Data Scientist

The senior data scientist is a member of the Data and Analytics Information Technology team, having a critical role in supporting the BHE (Berkshire Hathaway Energy) business transformations and asset performance management initiatives. The data scientist assists BHE business groups in translating business problems into technical and data requirements and participates in other Data and Analytics center of excellence initiatives. This position works with information technology teams across the company to extract, transform, validate and load data into data lake and analytics software systems including Microsoft Azure and Microsoft Power BI. This individual contributes to model development, model testing, production support, data validation and data integrity efforts in the course of daily work.

Provides Support to the Following Positions:

• Enterprise Analytics (IT), Berkshire Hathaway Energy
• Asset Performance and Investment Management, Berkshire Hathaway Energy
• Data Analytics Center of Excellence, Berkshire Hathaway Energy
• MidAmerican Energy Company

MidAmerican Energy Company, a Midwest utility, provides regulated electric and natural gas service to more than 1.6 million customers in Illinois, Iowa, Nebraska and South Dakota. The company owns and operates a portfolio of power-generating assets, approximately 61% of which is wind generation.

Education

• Bachelors degree in computer science, mathematics, software engineering or a related technical field.
• Masters in data science or related technical field preferred.

Experience

• Five or more years of experience in data science, with a proven track record of leading and delivering successful data science projects.
• Demonstrated hands‑on experience designing and building data science proof‑of‑concepts and working with data from multiple sources, including relational databases, APIs, and modern data platforms (e.g., Delta tables), using SQL, PySpark, and Python.
• Advanced proficiency in Python and/or R, with extensive experience using standard libraries for data wrangling (e.g., pandas, tidyverse), modeling (e.g., scikit‑learn, caret, tidymodels), and data visualization (e.g., seaborn, ggplot2).
• Established expertise in at least one core data science domain, such as supervised learning, time‑series analysis, or survival modeling, with the ability to apply these techniques to complex, real‑world business problems.
• Proven experience productionizing advanced analytics and machine learning models, including transitioning solutions from development to operational and production environments.
• Strong practical foundation in descriptive and inferential statistics, including hypothesis testing, confidence intervals, correlation analysis, and related statistical methods.
• Hands‑on experience with enterprise data visualization tools (Power BI preferred) and at least one cloud‑based data platform (Azure and Databricks preferred).
• Excellent verbal and written communication skills, with the ability to clearly communicate technical concepts, analytical results, and recommendations to both technical and non‑technical stakeholders across multiple levels of the organization.
• Strong leadership and interpersonal skills, with the ability to work independently, collaborate effectively within a team, and influence outcomes without direct authority.
• Demonstrated initiative and resourcefulness, with the ability to navigate ambiguity, prioritize work effectively, and deliver results with limited guidance in a fast‑changing environment.
• Experience working in cross‑functional team environments, partnering with engineering, product, and business stakeholders to deliver data‑driven solutions.
• Preferred experience with Spark, Azure DevOps, and MLOps practices.

Responsibilities

• Mentor and support a team of data scientists by providing technical guidance and thought leadership to ensure successful project execution, while fostering a collaborative and innovative team environment.
• Demonstrated experience in team leadership, project planning and management, and stakeholder engagement.
• Partner with Product Owners to lead end‑to‑end data science initiatives, from problem definition and data exploration through model development, validation, and deployment.
• Apply advanced statistical and machine learning techniques to analyze complex datasets, uncover meaningful patterns, and develop predictive and prescriptive models.
• Contribute to the companys data strategy by identifying opportunities to leverage data science to improve business processes, optimize operations, and drive revenue growth.
• Collaborate with stakeholders across the organization, including Performance Engineering, Asset Performance, and Transmission & Distribution, to understand data needs and deliver solutions aligned with business objectives.
• Stay current with advancements in data science, machine learning, and GenAI technologies; evaluate and help implement new tools, algorithms, and frameworks to enhance team capabilities.
• Ensure high‑quality documentation practices, including narrative documentation in wikis and technical documentation in version‑controlled repositories.
• Operate within an agile environment that supports iterative development and continuous delivery.
• Communicate effectively with both technical and non‑technical audiences across multiple platforms, including meetings, chat, email, and video conferencing.
• Effectively communicate with colleagues on both technical and non‑technical topics, across a variety of communications platforms, including voice and video calls, chat, and email.