Senior Data Scientist

Job Purpose

The Senior Data Scientist II is responsible for designing, validating, and productionizing forecasting and planning models that translate sales targets and media investment into digital marketing goals, website traffic forecasts, and budget allocation guidance. This role connects rigorous statistical and machine learning methods with business context so Auto Sales, Auto Strategy, Marketing, Web, and Media teams can set realistic targets and improve the efficiency of marketing investments across Honda and Acura.

Key Accountabilities

• Design, build, and validate statistical and machine learning models for forecasting and planning, using methods appropriate to the business question.

• Translate sales targets into digital marketing goals and planned media spend into website traffic and performance forecasts.

• Engineer features from sales, media, digital traffic, and business data. Validate results against baselines and guard against leakage, lookahead bias, and inconsistent definitions.

• Identify, evaluate, and integrate data sources that can improve forecast accuracy and reliability.

• Develop maintainable, production-quality code. Partner with engineering to deploy, monitor, and retrain models, and investigate forecast errors or model regressions.

• Assess structural changes in media spend, campaign strategy, seasonality, or demand patterns and adjust modeling assumptions when needed.

• Communicate findings, model limitations, tradeoffs, and recommendations to technical and non-technical stakeholders and leadership.

• Build productive working relationships across marketing, digital, sales, analytics, and engineering teams.

Qualifications, Experience, and Skills

Minimum Education

• Bachelor's degree in Statistics, Economics, Computer Science, Data Science, Engineering, or a related quantitative field required.
• Master's degree or PhD in a quantitative field preferred

Required Experience and Skills

• 5+ years of hands-on professional experience building, testing, and deploying data science or machine learning solutions.
• 5+ years of experience in Python and statistical programming for implementing statistical analysis and machine learning models.
• Proven experience deploying and operationalizing machine learning models and data pipelines into live production environments.
• Detailed knowledge of supervised and unsupervised learning, predictive modeling, NLP, time series, and frameworks such as PyTorch, TensorFlow, Scikit-Learn, or Spark.
• Experience developing forecasting models, including time series analysis, seasonality handling, trend decomposition, and performance evaluation.
• Experience with causal inference or econometric methods and interpreting model outputs in business terms.
• Proficiency in SQL and experience working with large relational datasets from multiple sources.
• Experience with cloud environments including AWS S3, EC2, Redshift, Glue, and SageMaker, and data frameworks including Spark, Hadoop, and HDFS. Familiarity with MLOps practices.
• Ability to scope ambiguous business problems, formulate hypotheses, and develop end-to-end solutions with minimal oversight.
• Strong model validation and analytical judgment, including the ability to identify leakage, spurious correlations, weak baselines, and implausible results.
• Ability to explain complex technical concepts and model outputs clearly to non-technical business stakeholders and build cross-functional working relationships.
• Experience with marketing mix modeling. Automotive or retail industry experience is a plus.
• Familiarity with digital marketing measurement, attribution, and the relationship among media investment, traffic, conversion, and business outcomes.