Job Description

Job Description

As a Data Scientist within our client's Digital Workplace area, you will be crucial in driving technical innovation and advancing our GenAI applications within the organization.

You will assist in designing, and implementing our client's GenAI solutions for our colleagues, ensuring optimal performance, productivity, and collaboration for our employees.

This key role will help transform how 300,000+ associates find information by transforming our internal knowledge base and enhancing employee productivity through RAG and GenAI. For the right technologist with diverse expertise, this could be the opportunity to redefine the meaning of a digital workplace for a respected global healthcare leader.

The ideal candidate is a hybrid between data scientist (75%) and software engineer (25%), with experience designing RAG systems, understanding optimization techniques, able to write microservices and deploy them using Kubernetes, and understand devops and CI/CD using GitHub Actions.

Previous experience designing advanced RAG systems is required. You should have a GenAI portfolio that highlights your experience.

As a data scientist on the Colleague Experience AI developer team, you will implement high-priority AI enterprise-level initiatives and work with the latest tools in the GenAI space.

Compensation:

$30/hr to $40/hr

Exact compensation may vary based on several factors, including skills, experience, and education.

Benefit packages for this role will start on the 1st day of employment and include medical, dental, and vision insurance, as well as HSA, FSA, and DCFSA account options, and 401K retirement account access with employer matching. Employees in this role are also entitled to paid sick leave and/or other paid time off as provided by applicable law.

We are a company committed to creating diverse and inclusive environments where people can bring their full, authentic selves to work every day. We are an equal opportunity/affirmative action employer that believes everyone matters. Qualified candidates will receive consideration for employment regardless of their race, color, ethnicity, religion, sex (including pregnancy), sexual orientation, gender identity and expression, marital status, national origin, ancestry, genetic factors, age, disability, protected veteran status, military or uniformed service member status, or any other status or characteristic protected by applicable laws, regulations, and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application or recruiting process, please send a request to HR@insightglobal.com.To learn more about how we collect, keep, and process your private information, please review Insight Global's Workforce Privacy Policy:

Skills and Requirements

• Bachelor's degree in Data Science, Computer Science, Machine Learning, Engineering, or related field from an accredited university.

• 2+ years of experience programming with Python.

• 1+ years of experience in Git and version control.

• 1+ years of experience with a public cloud (Azure preferred).

• 1+ years using Scikit-learn

• 1+ years of Langchain experience.

• 1+ years of LLM experience building RAG systems

• 1+ year experience with vector databases such as MongoDB Atlas or Pinecone

• Familiarity with concepts such as Agents, adding memory to LLMs, routing, moderating content, parsing output - Master's degree preferred.

• Excellent communication and planning skills.

• Portfolio of LLM applications and sample projects

• 2+ years of NLP experience using tools such as NLTK, SpaCy, and Beautiful soup

• 1+ years of LLM experience building RAG systems at scale (10,000+ documents).

• 1+ years experience in DevOps with Github Actions or similar CI/CD tools.

• 1+ years of writing and deploying microservices

• 1+ years of Kubernetes experience

• 1+ year analytics experience designing dashboards in tools like Tableau or PowerBI

• Strong code refactoring skills

• Strong debugging skills

• Must be located in Central or Eastern time zones

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