Senior Manager, Data Science

Role Overview

• Apply advanced analytics, machine learning, deep learning, and AI methodologies to build predictive models and intelligent automation systems that deliver measurable business impact.
• Own the full model lifecycle: data exploration, modeling, validation, deployment, monitoring, and continual improvement.
• Diagnose and resolve model performance issues; implement scalable, reliable processes for ongoing model operations.
• Use strong programming skills to prepare complex datasets (structured, semi‑structured, unstructured) and build reproducible pipelines.
• Translate complex analytical findings into clear, actionable insights that influence decision‑making at all levels.
• Present recommendations in a compelling narrative that resonates with both technical and non‑technical stakeholders.
• Lead discovery sessions to clarify business needs, define success metrics, and design analytical approaches that support business strategy.
• Drive innovation in Data & AI through new techniques, experimentation frameworks, and reusable analytical assets.
• Establish and promote best practices in model development, MLOps, governance, and data quality.
• Stay current on emerging trends, regulations, and technologies; provide guidance on how to leverage them within the organization.
• Serve as a subject matter expert for stakeholders across the enterprise.
• Partner with cross‑functional teams—including Engineering, Product, and Business—to deliver solutions that scale.
• Lead or contribute to strategic initiatives, roadmaps, and business cases that support the organization’s data and AI vision.
• Mentor and guide other data scientists; contribute to internal training, tools, and knowledge‑sharing.
• Manage complex, ambiguous problems by structuring analytical approaches and selecting appropriate modeling techniques.
• Lead change management and communication efforts associated with analytics initiatives.
• Implement processes and controls that ensure high‑quality, consistent delivery of analytical work.

Requirements

• 7+ years of relevant experience in data science, machine learning, or applied analytics.
• Advanced degree (PhD preferred) in Computer Science, Engineering, Statistics, Mathematics, Physics, or related quantitative field—or equivalent experience.
• Proven track record of shipping models to production and maintaining them at scale.
• Expertise in machine learning and deep learning algorithms and frameworks.
• Proficiency in Python and SQL; experience with R, SAS, or other analytical tools is a plus.
• Experience with distributed computing technologies (Hive, Hadoop, Spark) and cloud ML platforms (AWS SageMaker, Azure ML, etc.).
• Strong communication, storytelling, and influence skills; able to drive alignment across diverse groups.
• Demonstrated ability to operate in ambiguity, make data-driven decisions, and solve complex, open-ended problems.
• Experienced collaborator with deep cross-functional partnership skills and ownership mindset.

Tech Stack

• AWS
• Azure
• Cloud
• Hadoop
• Python
• Spark
• SQL

Benefits

• health insurance
• tuition reimbursement
• accident and life insurance
• retirement savings plans