Role
Payroll Data Scientist
Accenture Hiring Level
Level 10
As a Data Scientist in the Payroll domain, you will leverage data to optimize payroll operations, ensure compliance, detect anomalies, and support strategic decision-making. You will work closely with HR, Finance, and IT teams to build predictive models, automate reporting, and enhance payroll accuracy and efficiency.
- Data Analysis and Interpretation:
- Utilizing advanced analytical techniques to analyze payroll data, identify trends, anomalies, and opportunities for improvement.
- Use statistical methods and tools to find patterns, trends, and relationships.
- Develop predictive models for payroll forecasting, headcount planning, and cost optimization.
- Reporting and Dashboard Development:
- Designing and maintaining comprehensive dashboards and reports that provide actionable insights to stakeholders.
- Build automations to gather data from various sources like databases, APIs, web scraping, or sensors.
- Process Optimization:
- Collaborate with payroll operations teams to streamline processes and enhance efficiency based on data-driven recommendations.
- Translate complex data results into actionable business strategies.
- Process Support:
- Execute the process accurately and timely as hands on processor.
- Escalate issues and seek advice when faced with complex issues/problems.
- Creates a logical plan, realistic estimates and schedule for an activity or project segment.
- Ensure Local Work Instructions are followed and updated regularly and train the team members on process updates.
- Ensure process controls are in place; Maintain, validate and update process documentations.
- Perform “Root Cause Analysis” on issues faces and suggest appropriate corrective action for current remediation and future control.
- Must be able to propose process improvement ideas which can reduce time, improved accuracy or enhance controls
- Participates in Knowledge Transfer of any process and acquires in depth knowledge of process as an SME.
- Strengthen work relationship with Onshore Teams and other internal teams.
- Participate in conference calls and prepare ‘minutes of meeting’.
Background Requirements
Criteria
Minimum Requirement
Evaluation Methodology
Skills & Knowledge Required
Skills / Knowledge
Mandatory Vs Preferred
Measure of Skill Proficiency
Evaluation Methodology
Programming (Python, R, SQL)MandatoryMet Expectation /Not Met ExpectationPracticePayroll Knowledge
Preferred
Met Expectation /Not Met ExpectationInterviewStatistics & Math (Probability, regression, hypothesis testing)MandatoryMet Expectation /Not Met ExpectationPracticePayroll Systems (Example: Workday / Dayforce / SAP) and CRM/Workflow tools (SNOW / WQM / CRM etc.)
Preferred
Met Expectation /Not Met ExpectationInterviewMS Office
Preferred
Met Expectation /Not Met ExpectationPracticeMachine Learning (Classification, clustering, recommendation systems)MandatoryMet Expectation /Not Met ExpectationPracticeCommunication SkillsMandatoryMet Expectation /Not Met ExpectationInterviewData Modelling / Analytics (Example: PowerBI / Tableau / MS Access)MandatoryMet Expectation /Not Met ExpectationPracticeMicrosoft PowerAppsMandatoryMet Expectation /Not Met ExpectationPracticePower QueryMandatoryMet Expectation /Not Met ExpectationPracticeProblem solving skills (Analytical skills / Collaborative thinking/ Adaptable to change)MandatoryMet Expectation /Not Met ExpectationInterview
Note for Sourcing
Candidate meets the educational, industry & role specific experience criteria to be put across HR screening.
Note for HR Screening
Candidate meets the sourcing criteria & within the standard compensation bracket for the role, basic communication skills to be put through for written assessment
Note for Ops Interview
Candidate nearer to the target on skill/knowledge required should be put on hold and considered again depending on the pipeline of the resources available to meet the hiring targets
Note for Offer Management
Candidates fit the role requirement but above the standard compensation offered should be referred to delivery/transition lead for decision,
Education
Bachelor’s degree in Data Science, Statistics, Computer Science, Business Analytics, or a related fieldTo be verified through documents submitted at the time of interview
Total Years Of Experience
4 - 6 years of ExperienceTo be verified through documents submitted at the time of interviewTotal Years of Role Specific Experience3 - 4 years of Payroll Analytics ExperienceTo be verified through documents submitted at the time of interview
