Data Science Officer

Winnipeg, MB

Manitoba Hydro is consistently recognized as one of Manitoba's Top Employers! We are a leader among energy companies in

North America, recognized for providing highly reliable service and exceptional customer satisfaction. Join our team of Manitoba's

best as we continue to build a company that champions safety, supports innovation, and delivers on our commitment to customer

service - while actively fostering a diverse, equitable, and inclusive workplace reflective of the communities we serve.

Great Benefits

Competitive salary and comprehensive benefits package.
Defined-benefit pension plan for long-term financial security.
Nine-day work cycle, typically resulting in every other Monday off to support a balanced approach to work, family life and

community.

Position Overview

Reporting to the Enterprise Data & Analytics Lead, the Data Science Officer is responsible for planning, executing, and delivering

data science and advanced analytics solutions by modeling complex business problems through statistical, algorithmic, mining, and

visualization techniques. The focus of this role will be in machine learning (ML), artificial intelligence (AI), modelling and associated

data solution development, utilizing and enabling hypothesis-based problem analysis, data exploration and preparation, data

collection and integration, and operationalization for both everyday AI (optimization) and future AI (innovation). Further, this role will

support business leaders by creating business insights, reports, and analyses to aid in the decision-making process.

The Data Science Officer is expected to be visionary and strategic with an ability to turn innovative ideas for the utility industry into

real business value through an open mindset. The Data Science Officer also provides technical leadership in the development of

enterprise policies and strategies to leverage ML and AI to achieve business outcomes. There is a clear expectation that ML and AI

solutions will be developed and deployed using explainable, responsible and ethical practices to ensure these technologies are used

appropriately and to effectively manage associated risks and biases. Success also hinges on the ability to collaborate effectively with

a diverse range of stakeholders including other Data Scientists, Data Engineers, Data Developers, Data Architects, Enterprise

Architecture, Cyber Security, and Cloud Platform teams, to design and implement robust, secure, data science and advanced

analytics solutions.

Responsibilities

Designs and conducts data analyses with the highest standard of rigor and scientific accuracy, including study design,

methodology, algorithms, and statistical modeling

Performs large-scale experimentation and builds data-driven models to identify hidden relationships between variables in

large datasets and to answer business questions

Proactively mines data sources to identify trends and patterns and generates insights for business stakeholders and senior

leadership

Leads projects that implement complex ML and AI solutions, leveraging complex and advanced tools and techniques
Anticipates internal customers' short and long-term needs by proactively identifying Machine Learning (ML) and Artificial

Intelligence (AI) opportunities; innovates for utility industry solutions and shares these opportunities with business

stakeholders to validate relevance and value

Collaborates with business stakeholders, providing consultative advice and expertise to translate complex business needs into

analytics requirements to support business decisions

Develops and maintains strong working relationships while growing utility industry knowledge to assess longer-term more

strategic needs of internal customers across the organization

Grows knowledge through developing working relationships with industry and academic contacts to research classical and

cutting-edge techniques and tools in machine learning, deep learning, artificial intelligence, statistical analysis, and

visualization techniques and keep abreast of industry best practices

MANITOBA HYDRO IS COMMITTED TO DIVERSITY AND EMPLOYMENT EQUITY

Reference Code: CO57210441-01

Documents and mentors team members in guidelines and standards, ensuring process alignment, shares technical expertise,

provides training, performs code reviews, directs the execution of their tasks, and provides feedback on their technical

performance

Partners with D&T and business stakeholders to develop ML and AI policies and strategies
Delivers formal presentations to internal business stakeholders at various levels including executives

Qualifications

A four year degree in Computer Science, Data Science, Statistics, Artificial Intelligence, Applied Mathematics, or a related

quantitative field from a university of recognized standing with a minimum of five years general IT experience, including three

years of directly applicable Data & Analytics programming experience launching, planning, and executing data science

projects, including statistical analysis, data engineering, and data visualization

A two year diploma in Data Science, Statistics, Artificial Intelligence, or a related quantitative field with a minimum of seven

years general IT experience, including three years of directly applicable Data & Analytics programming experience launching,

planning, and executing data science projects, including statistical analysis, data engineering, and data visualization

Alternate experience and education in equivalent areas such as economics, engineering, or physics is acceptable; experience

in more than one area is strongly preferred

A specialization in ML, AI, cognitive science or data science is preferred
Microsoft and/or Databricks certifications in AI and ML preferred
Fluency in multiple programming languages and statistical analysis tools such as Python, Jupyter Notebook, C++, JavaScript,

R, Scala, SAS, Excel, SQL, MATLAB, SPSS

Experience with relational database programming languages including SQL and PL/SQL as well as nonrelational databases

such as NoSQL/Hadoop-oriented databases including MongoDB, Cassandra, etc.

Knowledge of distributed data/computing tools such as Spark, MapReduce, Hadoop, Hive, or Kafka
Experience working across multiple deployment environments including cloud, on-premises, and hybrid, and multiple

operating systems and containerization techniques such as Docker, Kubernetes, Azure, etc.

Strong understanding of AI domains such as ML, Generative AI, Optimization, Graphs, and Simulation, and their potential

roles in solving business problems such as prediction/forecasting, planning, computer vision, recommendation, natural

language processing, content generation, and knowledge discovery.

Experience in one or more of the following commercial/open-source data discovery/analysis platforms: RStudio, Spark,

KNIME, RapidMiner, Alteryx, Dataiku, H2O, SAS Enterprise Miner (SAS EM) and/or SAS Visual Data Mining and Machine

Learning, Microsoft AzureML, Databricks, IBM Watson Studio or SPSS Modeler, Amazon SageMaker, Google Cloud ML, SAP

Predictive Analytics

Experience in statistical and data mining techniques such as generalized linear model (GLM)/regression, random forest,

boosting, trees, text mining, hierarchical clustering, deep learning, convolutional neural network (CNN), recurrent neural

network (RNN), T-distributed Stochastic Neighbor Embedding (t-SNE), graph analysis, etc.

Experience in applying DevOps/MLOps methods to the construction of ML and data science pipelines
Knowledge of Responsible AI with demonstrated experience aligning to Responsible AI best practices
Understanding of Data Privacy regulations and best practices
Experience in DevOps and Agile (Scrum/Kanban), preferred
Willingness and ability to learn new technologies on the job
Ability to communicate complex projects, models, and results to a diverse audiences with a wide range of technical and

non-technical understanding

Ability to work in diverse, cross-functional teams in a dynamic business environment
Good presentation skills, including storytelling and other techniques to guide and inspire
Ability to create relationships quickly and strengthen relationships confidently
Demonstrated ability to be the technical lead on multiple projects or activities with competing priorities

Salary Range

Starting salary will be commensurate with qualifications and experience. The range for the classification is $47.22-$65.19 Hourly,

$90,484.68-$124,925.58 Annually.

Apply Now!

Ready to join a team that energizes Manitoba and puts safety, innovation, and inclusion at the heart of everything we do? Visit

www.hydro.mb.ca/careers to learn more about this position and to apply online.

Application deadline: JULY 21, 2026.

We appreciate your interest in Manitoba Hydro and thank all applicants. Only those selected for the next stage of the selection

Reference Code: CO57210441-01

process will be contacted.

If you require accommodations during the recruitment process or need this posting in an accessible format, please let us

know - we're committed to a barrier-free experience for all candidates.

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