Senior Data Scientist

Software Technology Inc.; United States; Remote; USD 120,000 - 160,000; Full time

Job summary

Software Technology Inc. is seeking a Data Scientist to join Stack AI for a remote role. The client is a medical device company developing a heart patch for arrhythmia detection; candidate must accommodate West Coast hours.

The role emphasizes building AI/ML/NLP solutions, leveraging foundation models, and delivering end-to-end cognitive computing projects with cloud and data workflows.

Qualifications

• Strong experience in AI and data science.
• Expertise in ML, LLMs, and foundation models.
• Hands-on NLP and cognitive computing.
• Ability to analyze structured and unstructured data.
• Experience architecting AI/Generative AI solutions.
• Advanced analytics and data-driven problem solving.
• Translate business requirements into scalable AI solutions.
• Experience delivering enterprise AI applications and integrations.
• Familiarity with AI tools like GitHub Copilot or CodeWhisperer.
• Strong communication and stakeholder management.

Job description

Position: Data Scientist - Stack AI ( Important)

Location: Remote (client sits on West Coast, so this person just needs to be open to working west coast hours)

This is with a new client!! – (it’s a medical device company; their product is heart patch for arrythmia detection).

Required Skills

• Strong experience in Artificial Intelligence (AI) and Data Science
• Expertise in Machine Learning (ML), Large Language Models (LLMs), and Foundation Models
• Hands-on experience with Natural Language Processing (NLP) and Cognitive Computing
• Ability to analyze and derive insights from structured and unstructured data
• Experience architecting, designing, and delivering AI/Generative AI solutions
• Strong background in advanced analytics and data-driven problem solving
• Ability to translate business requirements into scalable AI solutions
• Experience implementing enterprise AI applications and integrations
• Familiarity with GitHub Copilot, Amazon CodeWhisperer, or similar AI-assisted development tools
• Strong communication, stakeholder management, and business acumen skills
• A Data Scientist with expertise in Artificial Intelligence is a professional who skillfully combines data analysis and business acumen to tackle cognitive computing challenges. They are proficient in advanced analytics techniques for structured data and have a deep understanding of Natural Language Processing (NLP) and Machine Learning (ML) methods for unstructured content. They are specialized in foundation models and large language models and skilled in architecting and delivering AI solutions using cutting-edge technologies, with a strong focus on foundation models and large language models. In this role, you will be responsible for architecting and delivering AI solutions using cutting-edge technologies, with a strong focus on foundation models and large language models; along with Experience in tools like Github Copilot, Amazon Code Whisperer. This expertise enables them to create comprehensive cognitive solutions that effectively process and analyze both structured and unstructured data. The professional should also have a strong foundation in one of the following areas: NLP, Image Processing, Video Processing, Voice Processing, or Watson technologies. This skill set is valuable in solving complex business problems and leveraging the power of artificial intelligence for cognitive computing. They have strong programming skills, with proficiency in Python and experience with AI frameworks such as TensorFlow, PyTorch, Keras or Hugging Face; understanding in the usage of libraries such as SciKit Learn, Pandas, Matplotlib, etc; familiarity with cloud platforms (e.g. Kubernetes, AWS, Azure, GCP) and related services. They work with a variety of relational and NoSQL databases (SQL, Postgres, DB2, MongoDB), operating systems (Linux, Windows, iOS, Android), and modern UI frameworks (Backbone.js, AngularJS, React.js, Ember.js, Bootstrap, and JQuery). They have experience in full AI project lifecycle, from research and prototyping to deployment in production environments