Senior Data Scientist

Lineage Logistics•Detroit, MI

19d

About The Position

At Lineage, our Data Science team is transforming customer experience and commercial decision-making through AI and advanced analytics. We're seeking a Senior Data Scientist to lead high-impact initiatives across both areas. You'll apply machine learning to customer, sales, and operational data, partnering closely with business stakeholders to deliver solutions that improve customer experience and drive measurable business results.

Requirements

5+ years of applied data science experience (or a PhD plus equivalent applied experience), with a track record of owning models from prototype through production.
Strong hands-on skills in Python and SQL; experience with Snowflake and cloud infrastructure (AWS preferred) a plus
Proficiency in analyzing and interpreting complex datasets using statistical techniques, data modeling, and data visualization tools.
Experience building and validating machine learning models on messy, real-world operational data.
Experience integrating vendor GenAI/LLM APIs into production workflows .
Familiarity with SageMaker and/or MLflow a plus .
Demonstrated ability to work directly with vendors, engineering teams, and non-technical business stakeholders simultaneously.
Excellent written and verbal communication; able to develop a compelling narrative around technical results for both technical and executive audiences.
Comfort operating with general rather than detailed instruction on day-to-day work, and driving structure on ambiguous, newly scoped initiatives .

Responsibilities

Own predictive modeling initiatives that turn operational, commercial, and third-party data — including signal extracted via vendor GenAI/LLM APIs and NLP from unstructured sources like customer service interactions and news — into production-grade models that inform business decisions.
Translate customer- and workflow-specific logic behind our customer care automation into scalable, generalizable modeling approaches rather than one-off fixes.
Work directly with vendors and engineering partners to define requirements, validate model outputs against production behavior, and troubleshoot misclassifications.
Socialize findings and model outputs with commercial and operational leadership, translating technical results into clear, actionable narratives for non-technical audiences.
Support teammates on adjacent data science initiatives as capacity allows.
Ship work that is documented, reproducible, and maintainable by others on the team.