Lead Data Scientist

Role Summary

The Lead Data Scientist is a technical leader responsible for designing, building, deploying, and operating data science and machine learning solutions at scale. This role combines advanced modeling expertise with strong MLOps ownership, ensuring models move reliably from experimentation to production and deliver sustained business value.

Responsibilities

• Partner with business leadership and cross-functional teams to define, support, and operationalize business initiatives.
• Lead projects and mentor junior team members in an enterprise environment.
• Lead the design and development of predictive, classification, forecasting, and generative AI models using structured and unstructured data.
• Apply advanced techniques including tree-based models, time-series, deep learning, and NLP where appropriate.
• Design, execute, and evaluate A/B tests to measure an incremental impact on revenue.
• Own the end-to-end ML lifecycle: development, testing, deployment, monitoring, and retraining.
• Architect scalable dashboards in Power BI that handle high-cardinality dimensions using aggregations, extracts, semantic models, row-level security, and incremental refresh to monitor performance, trends and accuracy.

Requirements

• Bachelor’s degree in data science, Computer Science, Engineering, Statistics, Mathematics, Operations Research or a related quantitative discipline. Master's degree is preferred.
• 5+ years of experience applying data science or operations research to business decision-making, with a demonstrated track record of innovation and impact.
• Required Skills & Expertise:
  • 5+ years of hands-on experience and proficiency in Python and SQL for large-scale data analysis, modeling, and automation.
  • Hands-on experience with Azure and Fabric and the ability to lead end-to-end model development and deployment.
  • Familiarity with workflow orchestration tools (e.g., Apache Airflow).
  • Experience with MLOps / DevOps is a strong plus.
  • Experience deploying models via APIs, batch pipelines, or streaming architecture.
  • Computational Programming with Python including packages such as Pandas, scikit-learn, Pytorch/Tensorflow.
  • Statistical and predictive modeling experience.
  • Experience building data visualizations and dashboards using Power BI, including working with high-cardinality data.
  • Ability to clearly explain analytical approaches, model assumptions, results, and implications to technical and non-technical stakeholders.
  • Strong analytical thinking, attention to detail, and ability to manage multiple competing priorities in a fast-paced environment.

Benefits

• Employee Discount
• 401(k) Matching
• Growth Opportunities
• Education Assistance
• Health Benefits
• Parental Leave
• Paid Time Off
• Daily Pay
• Free Movies