Manager, Data Analytics - Wage & Hour Litigation Support

Martenson, Hasbrouck & Simon LLP•Brookhaven, GA

1d•$120,000 - $150,000

About The Position

MHS is seeking a full-time Data Analytics Manager to lead our Firm’s data analytics team. MHS is focused on growing the data analytics team which supports attorneys in wage and hour employment class action lawsuits and related litigation. This team analyzes large and complex employment datasets, supports defense strategy through data-driven insights, and coordinates the collection and organization of employment records and case-related facts to facilitate early resolution and/or advance cases through all stages of litigation. The ideal candidate combines strong quantitative and technical skills with practical litigation experience, excellent communication, and leadership capabilities. Martenson, Hasbrouck & Simon LLP is a national law firm focusing on labor and employment defense. MHS is headquartered in Atlanta, Georgia, and we have over 80 attorneys in our six offices located in Atlanta, Carlsbad, Chicago, Dallas, Orange County, Sacramento, and New York. Our attorneys work with some of the largest employers in the United States and have extensive experience providing day to day employment related advice and defending clients faced with any kind of labor and/or employment claim.

Requirements

5+ years of experience in data analytics, data science, or relative quantitative field.
Bachelor’s degree in a quantitative discipline required.
Experience managing a team and leading complex, multi-stakeholder analytics projects.
Advanced proficiency with data analysis tools such as SQL, Python, and/or R.
Strong experience with data visualization and reporting tools.
Solid understanding of relational databases, data warehousing concepts, and ETL processes.
Experience working with large, or incomplete datasets and creating defensible methodologies for handling data limitations.
Strong written and verbal communication skills, including preparation of clear documentation and summaries.
Excellent organizational skills, with the ability to manage multiple matters, competing deadlines, and shifting priorities.
High degree of professionalism, discretion, and judgment, particularly when handling sensitive and confidential information.

Nice To Haves

Master’s degree in Data Analytics, Data Science, Statistics, Applied Mathematics, Computer Science, Artificial Intelligence, or a closely related field preferred.
Prior experience in a litigation support, consulting, or law firm environment strongly preferred, particularly in wage and hour or employment-related matters.
Familiarity with wage and hour concepts (e.g., overtime, regular rate, exemptions, meal/rest breaks, rounding, off-the-clock work) preferred.

Responsibilities

Lead, mentor, and manage the data analytics team supporting wage and hour and other employment litigation matters.
Oversee project planning, prioritization, and resource allocation across multiple cases and deadlines.
Direct the collection, cleaning, integration, and analysis of large volumes of employment-related data (e.g., timekeeping, payroll, HRIS, scheduling, POS systems).
Design analytical frameworks to evaluate exposure, damages models, and class certification issues in wage and hour employment matters.
Develop and review statistical analyses (e.g., sampling plans, regression, variance analysis, pattern detection) related to hours worked, pay practices, meal/rest breaks, overtime, and off-the-clock work.
Collaborate closely with attorneys, paralegal, and outside vendors to align analytical work with overall defense strategy.
Assist in drafting or reviewing portions of expert reports and declarations related to data methodology, analysis, and findings.
Lead efforts to identify, collect, and organize employment records and business data relevant to wage and hour and other employment claims.
Coordinate with clients’ HR, payroll, IT, and operations teams to understand data structures, system changes, and record-keeping practices.
Investigate data anomalies and reconcile discrepancies between different data sources (e.g., timekeeping vs. payroll vs. scheduling systems).
Document factual findings related to business practices, policies, and operational realities that bear on class certification, liability, and damages issues.
Develop standardized tools, templates, and workflows to increase efficiency and consistency across matters.
Identify opportunities to use AI, machine learning, and advanced analytics to automate and enhance data review, anomaly detection, and pattern recognition.