Data Scientist - W2

Location: Plano, TX - Hybrid

Experience: 7 10 years

Role Overview

We re looking for an experienced Data Scientist to support our AI/ML-powered pricing engine. This role sits between data engineering and business analytics, building scalable data models, pricing insights, and analytics solutions that drive data-informed pricing decisions.

Key Responsibilities

• Design and build analytical data models and ELT pipelines using Databricks, PySpark, and SQL
• Develop pricing analytics including elasticity, competitive pricing, margin optimization, and recommendation outputs
• Create Tableau dashboards and enable self-service analytics for business stakeholders
• Orchestrate and monitor pipelines using Apache Airflow; ensure data quality, reliability, and performance
• Collaborate with data scientists to productionalize ML models via Amazon SageMaker
• Optimize queries and data processing for large-scale datasets
• Partner with product, pricing, and business teams to deliver actionable insights
• Document data models, logic, and best practices in Confluence

Required Qualifications

• 7 10 years of experience in analytics engineering, BI, or data analytics
• Strong SQL and dimensional data modeling expertise
• Advanced Python/PySpark and Databricks experience (Delta Lake, Spark SQL)
• Proven Tableau dashboard development experience
• Hands-on experience with Airflow and AWS (S3, Lambda, SageMaker)
• Knowledge of pricing analytics and revenue optimization concepts
• Experience with Denodo or similar data virtualization tools
• Strong communication skills and ability to translate business needs into analytics solutions
• Bachelor s degree required; Master s and relevant certifications preferred

===

Additional information:

Talent needs in data, analytics, and AI

• Have historically struggled to find talent within the data and analytics business organization, particularly for the data-and-AI work supporting the business unit.
• The client s expectations have shifted away from traditional staffing models toward more forward-deployed engineers who can do more than one thing.
• Desired profiles now include people who can handle development, testing, and other multi-skilled engineering work, rather than single-skill specialists.
• Identified specific hard-to-source technologies and capabilities, including:
• PostgreSQL and related database skills
• Fivetran, which he noted is especially difficult to source
• Broader AI-ready engineering capability, with an AI-first mindset focused on building agents and automating tasks through agentic workflows
• The business is starting to pivot traditional data engineering roles into hybrid data AI roles.
• Additional demand was identified in the data science area, including data scientists, MLOps engineers, and ML engineers.

Estimated immediate need at roughly 2 to 3 data science roles, and potentially 3 to 5 additional roles over the next 2 quarters as the organization shifts toward more AI-led operations.