Arbetsbeskrivning

Ericsson AB Join our Team About the Opportunity  We are an internal startup with a clear goal. We want to move beyond reporting to build data solutions and AI agents that leaders love to use. We are looking for a Data Scientist who wants to build products. You will not just provide numbers. You will build the intelligence engines and tools that make our 7000+ leaders more competitive. Your job is to bridge the gap between raw data and real business value. You will use Python, SQL, and AI to create solutions that are useful, appreciated, and solve actual problems for the business. You will work in an environment where you are expected to figure out the right solution, build a prototype, and make it work. What You Will Do Hands-on Building & AI Engineering: Design and iterate on LLM prompts, RAG pipelines, and agent workflows. You will build functional prototypes (ranging from Power BI dashboards to AI agents in Snowflake Cortex) to validate feasibility before scaling. Product Discovery & Fit: Partner directly with the Head of People Analytics to identify high-impact problems. You will talk to users to validate hypotheses and prioritize the features that deliver measurable business value, ensuring we solve real problems rather than just building technology. UX & Experimentation: Sketch user flows and define success metrics for MVPs (Minimum Viable Products). You will run pilots, track usage patterns, and iterate the product based on real-world behavior and feedback. Governance & AI Ethics: Be the guardian of Responsible AI within the build process. You will ensure our agents and workflows adhere to privacy standards by designing robust testing protocols to catch failure modes early. Data Modeling & Automation: Write complex SQL queries and Python scripts to power the data layer of our products. You will ensure the data feeding our AI models is clean, reliable, and optimized for consumption. The Skills You Bring •    Expertise in the core data stack, specifically SQL and modern visualization tools like Power BI or Sigma, with the ability to model complex datasets. •    Strong practical skills in Python for automation, data manipulation (Pandas), and exploratory analysis (Jupyter notebooks). •    Applied literacy in AI and LLMs, including an understanding of prompt engineering, RAG mechanics, and agent configuration (e.g., Snowflake Cortex). •    A "bias for action" mindset. You prefer shipping a rough prototype today over waiting for a perfect spec next week. •    Comfort with ambiguity and a product-thinking approach. You view failure as data collection and always ask "Why?" before "How?" •    Ability to communicate complex technical concepts into clear business value for HR stakeholders.