Top Data Science Jobs in Minneapolis, MN
The Staff LLM Data Scientist will design and fine-tune large language models for various applications, build advanced Retrieval Augmented Generation (RAG) pipelines, and implement evaluation frameworks for model performance. They will deploy models ensuring low latency and reliability, while collaborating with product and engineering teams.
Saviynt is seeking a Sr. Revenue Operations Analyst to drive full funnel accountability across the revenue engine. Responsibilities include optimizing data operations, translating business requirements, providing strategic insights, and enhancing sales enablement. Qualifications include 3-5 years of revenue or sales operations experience, expertise in Salesforce, strong communication and analytical skills.
Develop cutting edge AI technology to help people extract unparalleled value from meetings and conversations. Leverage NLP expertise to design neural net architectures and optimize training pipelines. Drive research agenda to develop new capabilities using large-scale conversational data.
The Senior Unified Communications Analyst will be responsible for resolving Unified Communications user incidents and requests, working with vendor-specific video teleconferencing systems, providing weekly reports, and assisting in the installation of data and voice communications equipment. This role requires a security clearance and full-time on-site support.
As a Data Scientist at Komodo Health, you will deliver high-value analytics projects, implement client solutions using statistical analysis and ML, develop an understanding of healthcare claims data, and create plans to scale solutions. You will present insights to partners and promote good engineering practices.
The Revenue Operations Analyst at TeamSnap is responsible for developing revenue forecasts, tracking key performance indicators, creating dashboards for stakeholders, and collaborating with sales, marketing, and finance teams to optimize revenue performance. The role involves providing data-driven insights, improving forecasting methodologies, and ensuring alignment on revenue goals.
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