Lead Data Scientist, Technology

• Design and develop statistical, machine learning, forecasting, and optimization models

• Apply techniques such as regression, classification, clustering, time series, and anomaly detection

• Translate business problems into scalable analytical approaches and measurable outcomes

• Evaluate model performance, accuracy, stability, and business impact

• Lead models from concept through production deployment and ongoing optimization

• Partner with engineering teams to operationalize models into applications and workflows

• Define and support model lifecycle processes, including versioning, monitoring, and retraining

• Build reusable, maintainable, and well-documented modeling pipelines

• Monitor model performance, drift, and usage; troubleshoot production issues as needed

• Collaborate with stakeholders to define objectives, constraints, and success metrics

• Identify and prioritize high-value data science opportunities

• Communicate results, assumptions, risks, and recommendations clearly to technical and non-technical audiences

• Support adoption by ensuring outputs are actionable, interpretable, and aligned to business needs

• Perform exploratory data analysis to identify patterns and opportunities

• Assess data quality, completeness, and suitability for modeling

• Design and validate features that improve model performance

• Partner with data teams to enhance analytical datasets and reusable data products

• Apply and promote best practices for reproducibility, model governance, and responsible AI

• Ensure alignment with enterprise standards for security, privacy, and compliance

• Mentor data scientists and analysts on modeling techniques and production readiness

• Lead technical and model reviews and contribute to data science standards and frameworks

Additional knowledge and skills:

• Experience with machine learning and statistical libraries (e.g., scikit-learn, XGBoost, PyTorch, TensorFlow)

• Familiarity with modern data platforms (e.g., Databricks, Snowflake, BigQuery)

• Experience with cloud environments (AWS, Azure, or GCP)

• Understanding of MLOps practices (model versioning, CI/CD, monitoring, lifecycle management)

• Experience building reusable modeling pipelines or scalable data science solutions

• Familiarity with tools such as MLflow, Git, and workflow orchestration platforms

• Exposure to model governance and responsible AI practices

• Experience with forecasting, optimization, pricing, or operational analytics is a plus

• Exposure to generative AI or LLM-based solutions is a plus

• Experience working in enterprise or matrixed environments is a plus