Sr. Data Scientist - Customer & Op. Analytics

Key Responsibilities

• Analyze customer, operational, monitoring, video classification, and theft-related data to identify trends, risks, opportunities, and actionable insights.
• Develop analytical frameworks to measure customer utilization, operational effectiveness, subscription adoption, and customer value realization.
• Support proactive customer engagement strategies through data-driven insights and trend analysis.
• Partner with leadership teams to improve visibility into operational and customer performance metrics.
• Design, build, and maintain predictive models related to theft trends, customer behavior, operational risks, service utilization, and escalation indicators.
• Develop forecasting and trend analysis models that support operational planning and customer success initiatives.
• Apply statistical analysis, machine learning, and advanced analytics techniques where appropriate to improve business outcomes.
• Continuously evaluate and refine model performance and business relevance.
• Develop dashboards, KPI reporting, and analytics tools using Power BI, Tableau, or similar platforms.
• Create executive-level reporting and operational scorecards that support strategic decision-making.
• Automate reporting and improve scalability of analytics and data visualization capabilities.
• Translate complex analytical findings into clear, business-oriented recommendations.
• Partner closely with Operations, Customer Success, Sales, Product Management, IT, Engineering, and Finance teams to identify business opportunities and analytics priorities.
• Support initiatives involving AI-driven analytics, workflow automation, and operational optimization.
• Collaborate with technical teams to improve data quality, accessibility, integration, and governance across systems and platforms.

Qualifications

• Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Business Analytics, or related field.
• 4–8 years of experience in data science, predictive analytics, customer analytics, operational analytics, or related analytical roles.
• Strong experience developing predictive models and performing advanced data analysis in business environments.
• Advanced proficiency in SQL and experience with Python or similar analytics/programming languages.
• Experience with Power BI, Tableau, or similar business intelligence and visualization tools.
• Experience working with large, complex operational and customer datasets.
• Strong analytical, problem-solving, and critical-thinking capabilities.
• Excellent communication and presentation skills with the ability to explain technical concepts to business stakeholders.
• Ability to operate independently and manage multiple priorities in a fast-paced environment.

Preferred

• Experience in SaaS, retail technology, video analytics, loss prevention, IoT, subscription-based services, or service-oriented organizations.
• Familiarity with machine learning, AI-driven analytics, and operational optimization techniques.
• Experience with cloud-based data platforms such as Azure, AWS, or Google Cloud.
• Experience supporting executive-level operational reporting and KPI development.