Telesat (NASDAQ and TSX: TSAT) is a leading global satellite operator, providing
reliable and secure satellite-delivered communications solutions worldwide to
broadcast, telecommunications, corporate and government customers for over 50
years. Backed by a legacy of engineering excellence, reliability and
industry-leading customer service, Telesat has grown to be one of the largest
and most successful global satellite operators.
Telesat Lightspeed, our revolutionary Low Earth Orbit (LEO) satellite network,
scheduled to begin service in 2027, will revolutionize global broadband
connectivity for enterprise users by delivering a combination of high capacity,
security, resiliency and affordability with ultra-low latency and fiber-like
speeds. Telesat is headquartered in Ottawa, Canada, and has offices and
facilities around the world.
The company’s state-of-the-art fleet consists of 14 GEO satellites, the Canadian
payload on ViaSat-1 and one LEO 3 demonstration satellite. For more
information, follow Telesat on X and LinkedIn or visit www.telesat.com
[http://www.telesat.com/]
We are seeking a Product Owner – Data Science & Modeling with deep expertise in
network data modeling (especially IETF YANG), machine learning, and business
analysis. This role is ideal for someone who can translate complex technical
challenges into actionable product strategies, enabling data-driven
decision-making across satellite and ground network systems.
You will design scalable data models and machine learning pipelines, apply ML
techniques in cloud environment, and work closely with stakeholders to
prioritize features and align product development with business needs. As the
key link between technical teams and strategic objectives, you’ll define and own
the roadmap for data-centric solutions that drive operational efficiency and
innovation.
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Main Responsibilities
- Own the end-to-end product lifecycle for data-driven features and models—from
ideation through development, deployment, and iteration.
- Design and oversee the development of scalable data models, with a focus on
network data standards such as IETF YANG.
- Translate business needs into clear data product requirements, ensuring
alignment between stakeholders, technical teams, and strategic goals.
- Define and prioritize product roadmaps, user stories, and backlogs in
collaboration with engineering, data science, and business teams.
- Develop and support machine learning pipelines for tasks like anomaly
detection, predictive maintenance, and performance optimization.
- Apply statistical and analytical methods (e.g., regression, clustering,
outlier detection) to extract insights and support business decisions.
- Collaborate with cross-functional teams, including data engineers, ML
researchers, domain experts, and executives, to deliver impactful solutions.
- Validate and measure the success of data initiatives using relevant KPIs, A/B
testing, and performance tracking frameworks.
- Ensure data governance, quality, and compliance throughout the modeling and
product development process.
- Act as a subject matter expert on data modeling and ML applications within
cloud off the shelf solutions and complex network infrastructures.
- Develop and maintain YANG models for telemetry and configuration data from
satellite and terrestrial networks.
- Design data models to support anomaly detection, predictive analytics, and
network optimization.
- Use data modeling tools to extract insights from high-volume telemetry data.
- Build real-time and batch data pipelines using Apache Kafka, Spark, and
Google Dataflow.
- Deploy ML pipelines in cloud and hybrid environments.
- Collaborate with business stakeholders to gather requirements, define KPIs,
and align data initiatives with strategic goals.
- Translate complex technical findings into clear business insights and
visualizations.
- Support data governance, lineage, and metadata integration across the data
lifecycle
Required Qualifications
- Bachelor’s or master’s degree in computer science, Data Science, Engineering,
Applied Mathematics, or a related technical field.
- 5+ years of experience in data science, data modeling, or cloud-based machine
learning, with at least 2 years in a product owner or technical leadership
role.
- Strong experience in network data modeling, especially using IETF YANG or
similar data modeling languages.
- Proficient in machine learning frameworks and in building ML pipelines in
production environments.
- Hands-on experience with statistical methods such as regression,
classification, clustering, and outlier detection.
- Familiarity with cloud platforms and data engineering tools (e.g., Spark,
Kafka, Airflow).
- Demonstrated ability to translate business needs into technical requirements,
and to manage a backlog and product roadmap.
- Excellent communication and stakeholder management skills, with experience
working cross-functionally between technical and non-technical teams.
- Experience with Agile methodologies, including sprint planning, backlog
grooming, and user story definition.
- Strong understanding of data governance, privacy, and security best practices
in complex or regulated environments.
- Experience defining or contributing to organization-wide AI strategy or
governance frameworks.
- Knowledge of AI/ML infrastructure at scale, including MLOps tools and model
monitoring strategies.
- Experience managing AI use case prioritization across multiple business
domains.
- Experience with NETCONF, RESTCONF, or gNMI. (Preferred)
- Knowledge of event-driven architectures and real-time analytics.
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The successful candidate must be able to work in Canada and obtain a Reliability
clearance.
At Telesat, we take pride in being an equal opportunity employer that values
equality in the workplace. We are committed to providing the best candidate
experience possible including any required accommodations at every stage of our
interview process. All qualified applicants that have been selected for an
interview that require accommodations, are advised to inform the Telesat Talent
team accordingly. We will work with you to meet your needs. All accommodation
information provided will be treated as confidential.