Job Type
Full-time
Description
Help our award-winning technology company run effectively as you take on big challenges and find solutions with a position in Operations. Use your problem-solving skills to shape the way others see Paylocity. Launch your career with us!
This is a fully remote position, allowing you to work from home or location of record within the U.S. with no in-office requirements. You must be available five days per week during designated work hours. The work arrangement for this role is subject to change based on business needs and individual performance. This may include adjustments to on-site requirements or schedule expectations, as necessary.
Position Overview This role helps operationalize machine learning and AI solutions that improve client outcomes, service efficiency, workforce productivity, and business decision-making. They partner closely with Data Scientists, Product & Technology teams, Data Engineering, Cloud Engineering, DevOps, and business stakeholders to deploy, monitor, and scale data science solutions across Paylocity's operational ecosystem.
They are comfortable working across MLOps, cloud platform engineering, and data engineering. The primary focus is not model research or product feature development. Instead, this role creates the repeatable pipelines, deployment patterns, monitoring practices, and CI/CD standards that allow operational models and AI solutions to be used reliably by the business.
Primary Responsibilities The below represents the primary duties of the position, others may be assigned as needed. To perform this job successfully, an individual must be able to perform each essential duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
- Partner with Operations Data Science to convert machine learning models, analytical prototypes, and AI solutions into scalable production-ready workflows.
- Design, build, and maintain automated data and model pipelines using Python, AWS, Databricks or similar cloud data platforms, Spark, and CI/CD practices.
- Develop reusable deployment patterns, templates, documentation, and engineering standards that help data scientists move solutions from experimentation to production more consistently.
- Collaborate with Product & Technology, Data Engineering, Cloud Engineering, DevOps, Delivery Platforms, and business operations teams to align Operations Data Science solutions with enterprise architecture, security, and platform practices.
- Create monitoring frameworks for model performance, data quality, drift, reliability, usage, and business impact measurement.
- Support responsible AI and model governance practices, including documentation, auditability, explainability inputs, validation workflows, and ongoing model health checks.
- Develop feature engineering pipelines, curated data assets, model-serving workflows, and operational data products that can support multiple use cases across the organization.
- Proactively identify and resolve issues, defects, or process gaps in machine learning pipelines, data workflows, and deployment automation.
- Translate operational business needs into technical requirements and communicate technical concepts clearly to non-technical stakeholders.
- Participate in cross-functional working sessions, provide feedback and technical recommendations, and help drive project execution across Data Science, business, and technology teams.
Education and Experience Requirements: - Bachelor's degree in computer science, data engineering, machine learning engineering, engineering, statistics, mathematics, data science, or another quantitative or technical field.
- At least 3 years of relevant experience in machine learning engineering, MLOps, cloud platform engineering, data engineering, software engineering, or similar technical roles.
- Experience building production-grade data or machine learning workflows in Python.
- Strong background with cloud data technologies, preferably AWS and Databricks, Spark, or similar platforms.
- Experience with CI/CD practices, deployment automation, version control, and software engineering fundamentals.
- Knowledge of data engineering concepts, including data pipelines, data quality, orchestration, feature engineering, and scalable processing.
- Demonstrated ability to partner with data science teams and translate analytical solutions into reliable operational workflows.
- Ability to work independently, deliver high-quality work, seek input from others, and contribute to shared standards and best practices.
- Strong communication skills, including the ability to explain technical concepts to business stakeholders and non-technical audiences.
Preferred Skills - Experience deploying, monitoring, or maintaining predictive models, forecasting systems, recommendation engines, NLP/text analytics, classification models, AI agents, and/or decision-support tools.
- Experience supporting machine learning solutions in operations, customer service, workforce management, CRM, business intelligence, or other business process environments.
- Familiarity with infrastructure-as-code, cloud security practices, observability tooling, model registries, workflow orchestration, or model monitoring platforms.
- Experience with responsible AI, model governance, explainability, auditability, risk management, or documentation practices for machine learning systems.
- Working knowledge of HR, payroll, HCM, service operations, client retention, or voice-of-client analytics.
- Interest in helping teams adopt practical MLOps standards, reusable tools, training materials, and operating practices.
Physical Requirements: - Ability to sit for extended periods: The role requires sitting at a desk or workstation for long periods, typically 7-8 hours a day.
- Use of computer and phone systems: The employee must be able to operate a computer, use phone systems, and type. This includes using multiple software programs and inquiries simultaneously.
The base pay range for this position is $140,000 - $163,000/yr; however, base pay offered may vary depending on job-related knowledge, skills, and experience. This position is eligible for a quarterly bonus and restricted stock unit grant based on individual performance in addition to a full range of benefits outlined here. This information is provided per the relevant state and local pay transparency laws for the location in which this position will be performed. Base pay information is based on market location. Applicants should apply via www.paylocity.com/careers.
