Staff Data Scientist

Job Description

Mission

Turn complex business questions and high-value data into trustworthy, production-grade machine-learning solutions that improve decisions, automate work, and create measurable business impact across Sales, Service, Marketing, and Global Markets.

This is a hands-on Staff Data Scientist role for an experienced individual contributor who can move seamlessly from business problem framing and analytical discovery to feature engineering, model development, production deployment, and continuous improvement. The role combines deep technical expertise with strong business judgment, helping teams adopt rigorous, interpretable, and reusable data-science practices at scale.
 

Key Responsibilities
 

Applied Machine Learning

Translate ambiguous business problems into clear analytical objectives, modeling strategies, and measurable success criteria.

• Develop, validate, and improve predictive, prescriptive, forecasting, optimization, classification, and segmentation models.

• Select appropriate statistical and machine-learning techniques based on the business decision, available data, operational constraints, and expected value.

• Apply advanced methods such as time-series forecasting, causal inference, experimentation, natural-language processing, and optimization when they are fit for purpose.
   

Data and Feature Engineering

• Define data requirements and partner with data engineering and business teams to establish reliable, well-documented data sources.

• Build scalable, reproducible feature pipelines and reusable analytical assets.

• Perform exploratory analysis, data-quality assessment, feature selection, and leakage detection to ensure models are based on sound data.

• Work across structured and unstructured data, including customer, vehicle, dealer, sales, service, warranty, incentive, and operational datasets.
   

Model Evaluation and Decision Quality

• Establish rigorous evaluation frameworks that reflect real-world business outcomes, not only offline technical metrics.

• Assess model performance, calibration, bias, interpretability, robustness, and operational fit.

• Explain model behavior, assumptions, limitations, and recommendations clearly to technical and nontechnical stakeholders.

• Design and analyze experiments, pilots, and champion/challenger approaches to validate value before broad adoption.
   

Production ML and MLOps

• Package and deploy models as reliable production services, batch processes, or decision-support capabilities in partnership with software, data, and platform engineers.

• Establish reproducible practices for dependency management, versioning, data lineage, experiment tracking, and model release management.

• Design model monitoring for accuracy, data quality, drift, latency, availability, and business performance.

• Define practical drift thresholds, automated alerts, retraining criteria, and service-level expectations for models operating in production.

• Investigate production issues, identify root causes, and improve models and pipelines through structured iteration.
   

Business Partnership and Delivery

• Collaborate with product leaders, business owners, architects, engineers, IT, Finance, and other partners to deliver end-to-end solutions.

• Connect technical work to measurable outcomes such as revenue growth, cost reduction, productivity, customer experience, risk reduction, or improved operational decisions.

• Balance analytical sophistication with usability, speed to value, maintainability, and adoption.

• Lead the data-science workstream from concept through production and continuous improvement, maintaining clear documentation and delivery accountability.
   

Technical Leadership and Enablement

• Serve as a technical authority and trusted advisor on machine learning, statistical modeling, experimentation, and production data science.

• Raise the quality bar for model development through reusable patterns, code reviews, documentation, testing, and reproducibility.

• Coach data scientists, analysts, engineers, and citizen builders on sound modeling practices and responsible use of AI.

• Help teams evaluate and use platforms such as Databricks, Azure AI, Glean, and other enterprise tooling when they accelerate delivery without compromising quality.

• Share lessons learned, reusable components, and practical guidance across the AI Center and partner organizations.
   

Required Qualifications

• Bachelor’s degree in Computer Science, Data Science, Statis