Data Scientist

Data Scientist – Analytics, Automation & Machine Learning

St. Louis, MO

Hybrid On-site

Compensation: $55 - $67 per hour (starting at; final offer may vary based on experience, skills, and internal equity)

Our Fortune 100 telecommunications client is seeking a dynamic Data Scientist specializing in analytics, automation, and machine learning to support Customer Operations and cross-functional product squads, including Device, Billing, UCM, Chat, Repair, and Mobile. As an embedded analytics partner, you will work closely with operations teams, product owners, and senior leadership to develop and maintain dashboards, reports, and Python-based automations using tools such as Python, Tableau, Splunk, and SQL.

This hybrid on-site role in St. Louis focuses on translating complex business challenges into actionable data solutions, building and optimizing data pipelines, and contributing to advanced analytics and machine learning projects. You will play a key role in data mapping, dashboard and report creation, automation of executive reporting, migration of legacy workflows, and support for predictive modeling and natural language processing initiatives.

The ideal candidate is a self-starter who can independently turn ambiguous business questions into practical data solutions and clearly communicate technical findings to both technical and non-technical stakeholders, including directors and VPs.

• Understand and map data from Splunk, SQL Server, Oracle (OAR), Snowflake, and Google Cloud Storage to business processes across Customer Operations.
• Write Python and SQL code to extract, transform, and load data from multiple enterprise systems into reliable, production-ready pipelines.
• Develop, maintain, and optimize Tableau dashboards and Splunk reports used by operations teams and senior leadership, ensuring quality across a large portfolio of analytics assets.
• Translate complex or ambiguous business questions into clearly scoped analytics problems, design solutions, and deliver actionable findings that support business decisions.
• Develop data validations, anomaly detection, and quality checks to ensure data populations and production pipelines remain complete and accurate.
• Automate recurring executive reporting and weekly status updates using Python, Jira REST APIs, Prefect, Windows Task Scheduler, and related tools.
• Support migration of legacy Alteryx workflows and Power Shell scripts to Python-based Prefect flows running in Docker containers.
• Contribute to predictive modeling and machine learning use cases such as agent fraud detection, Next Best Offer (NBO) targeting, operational forecasting, call listening, and reducing manual work.
• Apply statistical and machine learning techniques, including classification, regression, clustering, tree-based models, and time-series analysis, to identify trends, seasonal patterns, and anomalies in operational data.
• Support natural language processing initiatives such as sentiment analysis, intent classification, topic modeling, and embedding-based semantic search across large volumes of agent interactions.
• Help deploy and maintain analytical models through Git Lab CI/CD and Prefect; monitor model performance, identify drift, and refine solutions based on production feedback.
• Build lightweight internal applications and branded HTML reports using Streamlit and Python to make insights accessible to operations teams.
• Refresh and improve existing analytics in response to stakeholder feedback, evolving business needs, and measurable operational outcomes.
• Identify data quality risks, model drift, and pipeline failures, escalating issues to the Lead Data Scientist and executive sponsor as appropriate.
• Participate in Agile ceremonies as an embedded analytics partner; create Jira tickets with clear acceptance criteria, business rationale, and timelines, and own assigned work through completion.
• Communicate analytical findings, model insights, and recommendations clearly to technical and non-technical audiences, including product owners, operations teams, and senior leaders.
• Proactively clarify requirements, take ownership of end-to-end delivery, and support special projects and other duties as needed.

• Hands-on experience using Python, Tableau, Splunk, and SQL to build dashboards, reports, data workflows, and automations.
• Strong SQL skills and the ability to query, interpret, and work with data from multiple enterprise systems (e.g., Snowflake, Oracle, SQL Server, Google Cloud…