AI Data Scientist

About this opportunity

We’re seeking an AI Data Scientist for the Americas AI Acceleration Hub within Ericsson’s Strategic Projects & Transformation unit, collaborating closely with data engineers and solution architects to turn high‑volume datasets into actionable insights that drive decisions and outcomes. This role emphasizes strong grounding in traditional ML and statistical methods (e.g., classification, regression, forecasting, linear programming), with functional use of GenAI where it adds clear value.

You will primarily consume high‑quality data delivered by engineering teams and be accountable for the insights, models, and recommendations derived from that data.

• Transform large, complex datasets into clear, actionable insights using statistical analysis and machine learning (classification, regression, time‑series forecasting, and optimization via linear programming).
• Frame ambiguous business questions, propose analytical approaches, and deliver data‑backed recommendations — proactively identifying what insights matter without being prompted.
• Partner with data engineers to define data requirements and quality expectations, consuming curated pipelines from enterprise data lakes (e.g., Snowflake) to ensure reliable inputs for modeling.
• Prototype, evaluate, and operationalize models; emphasize interpretability and measurable impact over deployment alone.
• Apply GenAI selectively to augment workflows (e.g., text feature extraction, summarization, NLQ‑to‑insight prototypes, and data quality triage) and solutions; communicate tradeoffs between traditional ML and GenAI to non‑technical stakeholders.
• Validate data fitness for modeling (e.g., profiling, anomaly detection, leakage checks, and data quality rules) in partnership with data engineering.
• Collaborate with solution architects to integrate models into end‑to‑end systems that meet scalability, security, and compliance expectations.
• Communicate results through concise narratives, visuals, and “so‑what” recommendations that inform business strategy (e.g., segmentation and targeting strategies).
• Create repeatable prototypes and success stories that encourage adoption across teams and use cases.

• 6–8 years of professional experience in data science/ML, often progressing from data engineering into data science; demonstrated hands‑on impact is essential.
• Bachelor’s degree (or equivalent experience) in a quantitative field; emphasis on practical, hands‑on problem solving over pedigree alone.
• Strong proficiency in Python and/or R for modeling and analysis, including applied experience with statistical methods and core ML techniques (classification, regression, forecasting, optimization).
• Proven ability to derive insights from large datasets (hundreds of thousands to millions of rows) and translate findings into business actions.
• Experience working with data provided through enterprise pipelines and data lakes (e.g., Snowflake) in collaboration with data engineering teams.
• Clear, structured communication skills; ability to explain methods, assumptions, and trade‑offs to technical and non‑technical partners.

• Exposure to GenAI tools and APIs (e.g., OpenAI, Hugging Face) as a complement to traditional ML skills.
• Familiarity with cloud ecosystems used across the organization, in partnership with engineering.
• Domain exposure in telecom/network analytics or adjacent enterprise domains (e.g., operations, supply chain, finance) to contextualize features, constraints.
• Experience with experimentation and model evaluation frameworks.

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Ericsson uses a merit‑based hiring approach that values people with different experiences, perspectives and skillsets. We truly believe this approach drives innovation, which is essential for our future growth. We encourage people from all backgrounds to apply and realize their full potential as part of our Ericsson team. Ericsson is proud to be an Equal Opportunity employer, learn more.

If you need assistance or to request an accommodation due to a disability, please contact Ericsson at hr