Data Scientist
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Key skills for this role
About the Role
Ericsson is looking for a senior Data Scientist to design and implement agentic AI solutions, production-grade ML/GenAI systems, and robust data pipelines. You will work with LLMs, RAG, and MLOps to drive AI adoption across RAN management and software portfolio.
Key Skills for This Role
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) for 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
- Contribute to the design and evolution of the data platform, including data models, feature stores, metadata, and observability for AI/ML workloads
- Apply LLMs and GenAI techniques (prompt engineering, fine tuning, RAG) 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 with data management and governance stakeholders to ensure data used by AI/ML 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
- Collaborate with cross functional partners (engineering, product, operations, security, governance) to understand needs and translate them into robust data and AI solutions
Requirements
- Proven experience as a Data Scientist or similar role delivering production ML or GenAI solutions end to end
- Strong hands on skills with Python for data science and ML, and solid proficiency with SQL
- Practical experience with LLMs and GenAI applications including prompt engineering, tool augmented agents, fine tuning, or RAG
- Experience building or operating agentic AI systems (multi step tool using agents, workflow orchestration for LLMs, conversational task assistants)
- Solid understanding of data pipelines and data platform concepts (ETL/ELT, modern data platforms like Spark/Trino, data lakehouse formats, designing data models for AI/ML)
- Familiarity with MLOps practices (experiment tracking, model versioning, CI/CD for ML, monitoring, alerting)
- Experience with data management and governance principles (data quality, lineage, cataloging, access controls, privacy, compliance)
- Strong analytical and problem solving skills, ability to translate ambiguous problems into data and AI driven solutions
- Clear communication skills to explain complex technical concepts to non technical stakeholders
Full Job Posting
About This Opportunity
- Ericsson is accelerating the use of AI and GenAI across products. You will help shape how engineers and product teams use data, AI, and agentic workflows to drive better decisions, smarter automation, and faster innovation across RAN management and software portfolio.
- You will join a growing data and AI group building production grade data products and AI systems end to end, from raw telemetry and platform data to deployed AI agents and ML services.
What You Will Do
- 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.
The skills you will bring
- Proven experience as a Data Scientist or similar role, delivering production ML or GenAI solutions end to end, 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); designing
- 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 Have
- 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.
Compensation And Benefits At Ericsson
- Salary range for Ottawa: CAD 101,500 CAD 133,350 per year.
- Short Term Variable Compensation Plan: opportunity for an annual bonus based on business and individual performance.
- New employees receive a minimum of 18 days of accrued vacation, at least 3 personal days, minimum 10 holidays, 1 volunteer day, and sick days.
- Up to 10 weeks of paid maternity leave and 6 weeks of parental or adoption leave at 100% of pay.
- Additional benefits: financial wellness programs, educational assistance, matching gifts, wellness account, and recognition programs.
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