Manager, People Analytics & Data Science

Jobgether; United States; Remote; USD 140,000 - 210,000; Full time

Benefits offered by this job

Remote work from anywhere in the US

Health insurance

401(k) retirement plan

Job summary

Jobgether, on behalf of a partner company, seeks a Manager, People Analytics & Data Science in the United States to build and scale a trusted analytics capability that informs workforce decisions. This hands-on, strategic leader will connect analytics engineering, BI, and data science to create reliable pipelines, standardized metrics, and intuitive dashboards for leadership.

You will collaborate with HR, Finance, data science, and technology teams, advance governance, and drive analytics

Qualifications

• Bachelor’s degree or higher in a quantitative field.
• 7+ years in data science, people analytics, analytics engineering, BI, or data engineering.
• Experience with HR data, workforce planning, or organizational research.
• Proven ability to build data pipelines and analytics infrastructure.
• Proven experience delivering dashboards and self-service analytics.

Responsibilities

• Build and maintain reliable data pipelines and analytical datasets.
• Develop reusable data models, transformations, validation checks, and documentation.
• Lead end-to-end development of workforce analytics products and dashboards.
• Translate business questions into data requirements and metrics, with clear ownership.
• Partner with stakeholders to translate findings into practical decisions.

Skills

SQL

Python

Data pipelines

Analytics engineering

Stakeholder communication

Education

Bachelor's degree in data science, statistics, computer science, mathematics, engineering, economics, business analytics, or a related field

Job description

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a Manager, People Analytics & Data Science based in the United States.

The Manager, People Analytics & Data Science will build and scale a trusted analytics capability that helps leaders make informed, data-driven workforce decisions.

This role combines analytics engineering, business intelligence, and advanced data science to transform complex workforce data into actionable insights.

You will develop reliable data pipelines, standardized metrics, and intuitive dashboards that make people data accessible and useful across the organization.

Through statistical analysis, forecasting, and predictive modeling, you will investigate critical workforce questions and identify emerging trends and risks.

Working closely with HR, Finance, data science, and technology teams, you will establish strong data governance and responsible analytics practices.

This is an opportunity for a hands-on, strategic leader to shape scalable analytics products and strengthen the connection between workforce insights and business outcomes.

The position is remote within the United States, although an in-person interview may be required during the hiring process.

Accountabilities

• Data foundation and analytics engineering
  • Partner with data engineers to build and maintain reliable data pipelines and analytical datasets integrating workforce, organizational, operational, and business information.
  • Develop reusable data models, transformations, validation checks, documentation, and monitoring to ensure consistent, high-quality analysis.
  • Collaborate with HR technology, enterprise data science, analytics, Finance, and other technical teams on data architecture, system integrations, access, and data quality.
  • Translate business questions into clear data requirements, definitions, calculation logic, refresh schedules, and ownership standards.
  • Establish and maintain trusted metric definitions and improve the reliability, transparency, privacy, and usability of people data.
• Dashboards and self-service analytics
  • Lead the end-to-end development of workforce analytics products, from requirements gathering and prioritization to launch, adoption, and continuous improvement.
  • Build operational and executive dashboards that connect people metrics to workforce performance and broader business outcomes.
  • Design intuitive self-service experiences that help leaders understand what has changed, identify contributing factors, and determine appropriate next steps.
  • Integrate relevant insights into existing reporting tools and business routines to support timely, informed decisions.
  • Track adoption, trust, usefulness, and user feedback to improve analytics products and ensure they deliver measurable value.
• Research, advanced analytics, and predictive insights
  • Investigate priority workforce topics, including employee attrition, critical-role readiness, workforce capacity, planning, manager effectiveness, performance, productivity, and AI-enabled work.
  • Apply statistical analysis, experimentation, forecasting, segmentation, and predictive modeling according to the business question and available data.
  • Translate complex analytical findings into clear recommendations, explaining limitations, implications, and levels of uncertainty.
  • Apply appropriate validation, explainability, privacy safeguards, and bias-awareness to analytical methods and models.
  • Establish repeatable research practices, from hypothesis development and analysis to peer review, communication, and impact measurement.
  • Partner with stakeholders to translate findings into practical decisions, targeted interventions, and measurable outcomes.
• Partnership and capability development
  • Build strong working relationships with People leaders, business stakeholders, Finance, HR technology, enterprise data science, and other cross-functional partners.
  • Develop and maintain an analytics roadmap balancing foundational data infrastructure, self-service reporting, and targeted research.
  • Establish practical standards for data quality, testing, version control, documentation, access, and responsible use of sensitive workforce information.
  • Help leaders and People partners build confidence and capability in interpreting workforce metrics and applying insights.
  • Contribute to a collaborative, high-performing People Operations, Technology, and Analytics team while leading through influence.

Requirements

• Bachelor's degree in data science, statistics, computer science, mathematics, engineering, economics, business analytics, or a related field.
• At least 7 years of experience in data science, people analytics, analytics engineering, business intelligence, data engineering, or a related discipline.
• Experience working with people analytics, HR data, workforce planning, talent management, compensation, performance, or organizational research.
• Demonstrated ability to build data pipelines, analytical datasets, data models, or reusable analytics infrastructure.
• Proven experience developing dashboards and self-service analytics products for business stakeholders.
• Experience applying statistical analysis, experimental design, forecasting, segmentation, or predictive modeling to complex business problems.
• Strong hands-on proficiency in SQL and Python or R, with experience in modern data pipelines, data transformation, testing, and analytical workflows.
• Knowledge of data modeling, semantic layers, business intelligence tools, cloud data platforms, version control, and production-quality documentation.
• Solid understanding of statistics, causal reasoning, experimental design, predictive modeling, and model evaluation.
• Ability to distinguish between reporting requirements, diagnostic analysis, and deeper research or modeling questions, selecting an appropriate level of rigor for each.
• Strong communication and storytelling skills, with the ability to explain complex findings clearly and make evidence-based recommendations to senior leaders and non-technical audiences.
• A product-mindful, user-centered approach that starts with business decisions and designs analytics solutions around stakeholder needs.
• Excellent collaboration, curiosity, pragmatism, and sound judgment when working across business and technical teams.
• Strong discretion in handling confidential, sensitive, and personally identifiable employee information.
• Familiarity with AI and collaboration tools, with an understanding of responsible use.
• Comfort working through ambiguity, establishing practical standards, and continuously improving analytical quality.

Benefits

• Salary: Compensation varies by state and depends on experience, skills, and location:
  • California: typical hiring range of $171,148-$209,181 per year.
  • New Jersey, New York, Washington, Hawaii, Alaska, Maryland, Connecticut, Rhode Island, and Massachusetts: $128,136-$252,194 per year.
  • Nevada, Oregon, Arizona, Colorado, Wyoming, Texas, North Dakota, Minnesota, Missouri, Illinois, Wisconsin, Florida, Georgia, Michigan, Ohio, Virginia, Pennsylvania, Delaware, Vermont, New Hampshire, and Maine: $116,487-$229,267 per year.
  • Utah, Idaho, Montana, New Mexico, South Dakota, Nebraska, Kansas, Oklahoma, Iowa, Arkansas, Louisiana, Mississippi, Alabama, Tennessee, Kentucky, Indiana, South Carolina, North Carolina, and West Virginia: $104,839-$206,340 per year.
• Work flexibility: Remote work from anywhere in the United States for most positions.
• Paid time off: Vacation and sick leave, nine paid company holidays, and volunteer hours.
• Bonus opportunities: Potential holiday, referral, and performance-based bonuses.
• Healthcare and insurance: Medical, dental, vision, life, and pet insurance.
• Retirement planning: 401(k) retirement savings plan with company matching.
• Learning and development: Education assistance, professional development, and personal growth opportunities.
• Career progression: Opportunities for advancement and internal promotion.
• Recognition and workplace culture: Employee recognition programs and a collaborative work environment.
• Wellbeing support: Health and wellness resources, including free mental wellbeing therapy and coaching sessions, as well as childcare and eldercare resources.