Lead Data Scientist, Technology

The Lead Data Scientist is responsible for designing, developing, and deploying production-ready data science and machine learning solutions that drive measurable business outcomes. This role operates as a senior individual contributor and technical lead, partnering closely with business, data, engineering, and platform teams to translate complex problems into scalable analytical solutions.

The position owns the full lifecycle of modeling initiatives, from problem definition and model development through deployment, monitoring, and continuous improvement. This role also helps establish standards, best practices, and repeatable patterns to mature enterprise data science capabilities.

• 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

• 7+ years of experience in data science, machine learning, statistics, or a related field

• Advanced degree in a quantitative field (preferred)

• Proven experience developing and deploying models in production environments

• Experience leading complex analytical initiatives from problem definition through adoption

• Strong proficiency in Python and/or R for modeling and production-quality code

• Strong SQL skills for data exploration and dataset development

• Experience working in cross-functional environments (engineering, analytics, business teams)

• Ability to communicate complex concepts to non-technical stakeholders