Senior Data Scientist 

MUST be able to convert with the client WITHOUT sponsorship, or a work authorization that expires – so USC, GC, H4, GC EAD. They must also live in the Denver, CO area currently, no relocation.

Details:

Data Scientist

Location: Greenwood Village, CO - 4X ONSITE PER WEEK

Duration: 6+ month C2H - MUST BE ABLE TO CONVERT TO FTE AFTER 6 MONTHS WITHOUT SPONORSHIP

Interview process: 2 rounds, live coding in SQL and Python

Position Description

Description of project: The NLP team utilizes machine learning techniques and AWS services to support call center data in a production pipeline. The model output is used to gain insights from the call data and drive product development.

Day to Day/Project:

Supporting a project called Sorting Hat. It's a sorting system meant to categorize (ecaf tickets ( escalated customer issues that are complex but low in volume ) into

Reliability issues (i.e. service quality)

Process issues (i.e. was charged twice).

First phase of this is to provide a more detailed investigation using LLM to summarize these tickets, group them, and assign them to the categories. Last phase is to create customer surveys, source issues on social media, and understand a customer's call volume.

They need someone who has some data engineering skills like rank, sort, group. They also need to understand the business reasons / have an analytical mindset to be able to provide meaning to the many data sources they are working with in production. Someone who is operations based and likes process improvement. Computer science knowledge, as well.

Requirements

• Top 3 Requirements:

• Good tenure in a role as a data scientist – 5+ years of experience.

• Wants E2E data modelling, has implemented and deployed them, can say how they're benefiting the company, etc.

• SQL + Python + AWS

• Intangibles:

• Great personality (curious about data, can present to stakeholders, very sharp, eager to create solutions to problems)

• Can understand the business side of the data they are working with, have an analytical mindset

• PROACTIVE (create their own data models, not wait for someone to tell them to create a data model & how to do it)