Senior Data Scientist

Role & Responsibilities

Examine customer data, operational metrics, monitoring outputs, video classification results, and theft-related information to surface patterns, risks, and actionable opportunities.

Develop structured analytical approaches to assess product adoption, customer engagement, operational efficiency, and overall value delivery.

Enable more proactive customer engagement through data-backed insights and trend identification.

Work closely with leadership to enhance transparency into both operational effectiveness and customer performance indicators.

Create, deploy, and maintain predictive models focused on areas such as theft behavior, customer usage patterns, operational risk factors, service performance, and escalation triggers.

Build forecasting and trend analysis solutions to support planning, forecasting, and customer success strategies.

Leverage statistical techniques, machine learning models, and advanced analytics methods to drive better business outcomes.

Continuously monitor, tune, and improve model accuracy, relevance, and overall performance.

Design and build dashboards, KPI tracking tools, and reporting solutions using platforms like Power BI, Tableau, or comparable technologies.

Produce executive-level reports and operational scorecards that support high-level strategic planning.

Automate manual reporting processes and enhance the scalability of visualization and analytics tools.

Convert complex datasets and analytical outputs into clear, concise, and actionable business insights.

Partner with teams across Operations, Customer Success, Sales, Product, IT, Engineering, and Finance to identify high-impact opportunities and prioritize analytics initiatives.

Contribute to projects involving AI-based analysis, workflow automation, and operational efficiency improvements.

Collaborate with engineering and data teams to improve data integrity, accessibility, system integration, and governance practices.

Must Have Skills

• Bachelor’s degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, Business Analytics, or a related discipline.
• 4–8 years of hands-on experience in data science, predictive analytics, customer or operational analytics, or similar roles.
• Demonstrated experience building predictive models and performing sophisticated data analysis in a business environment.
• Advanced SQL expertise along with strong proficiency in Python or related programming tools.
• Experience using business intelligence platforms such as Power BI, Tableau, or similar tools.
• Experience working with large-scale, complex datasets across operational and customer domains.
• Strong analytical thinking, problem-solving capabilities, and attention to detail.
• Excellent communication skills with the ability to clearly explain technical findings to non-technical stakeholders.
• Proven ability to work independently while managing multiple priorities in a dynamic environment.

Nice To Have / Preferred Skills

• Exposure to SaaS environments, retail technology, video analytics, loss prevention systems, IoT platforms, subscription-based services, or service-driven organizations.
• Understanding of machine learning techniques, AI-enabled analytics, and operational optimization approaches.
• Experience working with cloud-based ecosystems such as Azure, AWS, or Google Cloud.
• Background in developing executive-level dashboards and KPI reporting frameworks.