Data Scientist

Ericsson•Ottawa, ON

8d•CA$101,500 - CA$133,350•Onsite

About The Position

Ericsson is accelerating the use of AI and GenAI across our products and how we build them. In this role, you will help shape how engineers and product teams use data, AI, and agentic workflows every day—driving better decisions, smarter automation, and faster innovation across our RAN management and software portfolio. You will join a growing data and AI group that is building production-grade data products and AI systems end-to-end, from raw telemetry and platform data to deployed AI agents and ML services used by internal users worldwide. Our data platform ingests large-scale RAN and software lifecycle data and provides the foundation for analytics, automation, and AI/ML. As a Data Scientist, you will be a senior individual contributor working closely with a lead engineer. You will focus on designing and implementing agentic AI solutions, robust data pipelines, and solid data management and governance practices that enable scalable, reliable, and compliant use of data across our organization.

Requirements

Proven experience as a Data Scientist or similar role, delivering production ML or GenAI solutions end-to-end (from data to deployed systems), ideally in a complex platform or product environment
Strong hands-on skills with Python for data science and ML, and solid proficiency with SQL for working with large datasets and analytical queries
Practical experience with LLMs and GenAI applications, including some of the following: Prompt engineering and tool-augmented agents, Fine-tuning or adaptation of foundation models, Retrieval-Augmented Generation (RAG)
Experience building or operating agentic AI systems (e.g., multi-step tool-using agents, workflow orchestration for LLMs, conversational task assistants)
Solid understanding of data pipelines and data platform concepts, for example: Building and operating ETL/ELT pipelines for large-scale structured and semi-structured data, Working with modern data platforms (e.g., Spark, Trino/Presto, data lakehouse formats such as Iceberg/Delta/Parquet, or equivalent technologies), Designing data models and features to support AI/ML workloads
Familiarity with MLOps practices, including: Experiment tracking and model versioning, CI/CD for ML, automated training and deployment, Online/offline evaluation, monitoring, and alerting for models and AI agents
Experience with data management and governance principles, such as: Data quality, lineage, and cataloging, Access controls, privacy, and compliance considerations, Working with governance frameworks and stakeholders to implement policies in practice
Strong analytical and problem-solving skills, with the ability to translate ambiguous or open-ended problems into clear, data- and AI-driven solutions
Clear communication skills: able to explain complex technical concepts and trade-offs in simple terms to engineering, product, and business stakeholders
A collaborative, proactive mindset and comfort working under a lead engineer while driving your own workstreams independently

Nice To Haves

Experience with software engineering practices (version control, code review, testing) and modern development stacks (e.g., containerization with Docker/Kubernetes, cloud-native architectures)
Exposure to streaming data technologies (e.g., Kafka, Spark Streaming, Flink)
Experience with metrics, telemetry, or CI/CD data related to software development processes
Familiarity with telecom, RAN, or network management domains
Master’s degree in Data Science, Computer Science, Statistics, or a related field, or equivalent practical experience

Responsibilities

Design and build agentic AI workflows and systems that orchestrate tools, data, and models to improve engineering, operations, and product development processes
Develop and deploy production-grade ML and GenAI solutions (including LLM-based agents) that support use cases such as developer assistance, intelligent automation, monitoring, and decision support
Collaborate with data platform and engineering teams to design, implement, and operate reliable data pipelines and features for AI/ML use cases (batch and, where relevant, streaming)
Contribute to the design and evolution of our data platform, including data models, feature stores, metadata, and observability for AI/ML workloads
Apply LLMs and GenAI techniques (prompt engineering, fine-tuning, Retrieval-Augmented Generation) to build practical, secure, and robust AI applications
Implement and follow MLOps best practices, including experiment tracking, model versioning, automated deployment, monitoring, and A/B testing
Work closely with data management and governance stakeholders to ensure that data used by AI/ML and agentic workflows meets quality, lineage, privacy, and access control requirements
Analyze complex datasets to derive actionable insights that inform platform evolution, process optimization, and AI/ML roadmap priorities
Document solutions, patterns, and best practices so other teams can build on your work and adopt the data platform and AI capabilities effectively
Collaborate with cross-functional partners (engineering, product, operations, security, governance) to understand needs, frame problems, and translate them into robust data and AI solutions