JOB PURPOSE: 

The Operations Data & Investigations Supervisor is responsible for overseeing daily data analytics activities to support operational efficiency and business decision-making. The role involves managing the development of data standards, deploying automation tools, supervising the construction and analysis of datasets, and ensuring data is clean, accurate, and actionable. This position plays a critical role in transforming raw data into strategic insights through statistical analysis, visualization, and cross-departmental collaboration to inform investigations and operational improvements.

RESPONSIBILITIES/DUTIES Data Analytics Oversight

• Supervise daily data analytics operations to ensure timely and accurate delivery of datasets that inform operational and business strategies.

• Oversee the construction of data sets to support trend analysis, strategic insights, and informed decision-making across the organization.

• Lead efforts to build processing capacity and develop team capabilities in statistical modeling, analytical methodologies, and data interpretation. Data Standards and Automation

• Develop and implement data standards to ensure consistency, reliability, and integrity of data used in operational analysis.

• Deploy and manage automation tools for efficient extraction, transformation, and loading (ETL) of data from various internal and external sources.

Data Preparation and Analysis

• Manage the data cleaning and preparation process, including filtering, managing missing data, and formatting datasets for accurate analysis.

• Conduct data exploration and statistical analysis to uncover patterns, relationships, and emerging trends that support continuous improvement initiatives. Data Visualization and Reporting

• Create visual representations of data insights through charts, graphs, and dashboards to enhance data comprehension and stakeholder engagement.

• Prepare clear and concise reports and presentations that translate complex data findings into actionable recommendations for decision-makers. Cross-Functional Collaboration

• Collaborate with various departments to understand their data needs, translate business requirements into analytical solutions, and support data-driven decision-making.

• Act as a key liaison between operations, investigations, and business units to ensure data analytics efforts align with organizational objectives.

ESSENTIAL QUALIFICATIONS, KNOWLEDGE & EXPERIENCE QUALIFICATIONS:

• Bachelor’s degree in Engineering, Computer Engineering, or a related field.

• High proficiency in English (spoken and written) is required. KNOWLEDGE:

• Solid foundation in statistics and data interpretation.

• Proficient in tools such as Excel, SPSS, SAS, Power BI, Tableau, and SQL.

• Skilled in data analysis languages (e.g., Python, R).

• Familiar with data preparation techniques, including cleaning and handling missing values.

• Understanding of basic machine learning concepts for predictive analysis.

• Capable of developing dashboards and reports for business insights.

• Experienced with database systems and data visualization best practices. EXPERIENCE:

• 3 to 5 years of relevant experience in data analytics, or operations analysis, preferably within a supervisory or coordination role.

DESIRED BEHAVIORS & EXPERIENCES

•Analytical Mindset: Approaches problems with data-driven thinking, using evidence and analysis to guide decisions.

•Attention to Detail: Ensures data accuracy, quality, and completeness in all stages of analysis and reporting.

•Proactive Leadership: Anticipates challenges in data management and takes initiative to improve processes and tools.

•Effective Communicator: Clearly conveys complex data insights through visualizations, reports, and presentations tailored to various stakeholders.

•Collaborative Attitude: Works closely with different teams to understand their needs and provide actionable data support.

•Adaptability: Responds flexibly to shifting priorities, data demands, and evolving business requirements.

•Integrity and Confidentiality: Handles sensitive data with professionalism, ensuring ethical standards and data privacy are maintained.

•Continuous Improvement: Seeks opportunities to enhance data analytics capabilities and adopt best practices in tools, methods, and technologies.