Lead Data Scientist
About The Role
We are looking for a Lead Data Scientist to join our Decision Intelligence Group. This hands-on role works closely with business partners to identify opportunities where innovative data science techniques can dramatically advance the business's objectives. You'll transform priority opportunities into incremental business value through the design, development, and deployment of statistical and machine learning solutions, working directly with senior leaders in a high-visibility role with tangible business impact.
Key Responsibilities
Building Business Relationships: Partner with business leaders to understand their strategy, key initiatives, and KPIs, taking shared ownership of reaching their goals.
Applying Analytical Techniques: Identify and apply the best analytical methods to achieve business objectives.
Designing and Deploying Solutions: Build advanced analytics solutions — including predictive and prescriptive outputs, insights, and visualizations — that enable data-driven decision-making.
Executing Analytics Activities: Handle data gathering, cleansing, integration, statistical transformation, modeling, and feature engineering/selection.
Developing Data Expertise: Build deep understanding of internal and external data sources.
Standards and Quality: Create standards and processes to ensure quality and consistency of Decision Intelligence Group solutions.
Leading Project Teams: Direct the activities of associate/senior data scientists, third-party contractors, and consultants on specific business projects.
Collaboration and Confidentiality: Contribute to team efforts, protect company confidentiality, and comply with health and safety guidelines.
Requirements
Education: Master's degree in data science, statistics, mathematics, econometrics, engineering, or another quantitative field.
Experience: 5+ years of relevant work experience.
Skills: Expert knowledge of statistics and machine learning methods (regression, classification, time series analysis, clustering, simulation, dimension reduction); proficiency in Python, R, and SQL; experience with data science tools (e.g., Databricks), distributed compute, large-scale data manipulation, and cloud-based analytics (e.g., Azure).
Preferred Qualifications
Education: Must have a masters and prefer a doctorate in data science, statistics, mathematics, econometrics, engineering, or another quantitative field.
Experience: Agile methodologies; data visualization tools (e.g., Power BI); IoT, software-defined customer experiences, or private equity–backed companies.
Benefits
Competitive Salary: Base salary commensurate with experience, plus performance-based incentives.
Health and Wellness: Comprehensive medical, dental, and vision plans, flexible spending accounts, and wellness programs.
Work-Life Balance: Flexible work arrangements, generous paid time off, and paid parental leave.
Professional Development: Opportunities for skill-building, leadership development, tuition reimbursement, and mentorship programs.
Additional Perks: Access to employee resource groups, community involvement opportunities, and retirement savings plan with company match.