Lead Data Scientist

TriNet is a leading provider of comprehensive human resources solutions for small to midsize businesses (SMBs). We enhance business productivity by enabling our clients to outsource their HR function to one strategic partner and allowing them to focus on operating and growing their core businesses. Our full-service HR solutions include features such as payroll processing, human capital consulting, employment law compliance and employee benefits, including health insurance, retirement plans and workers’ compensation insurance.

TriNet has a nationwide presence and an experienced executive team. Our stock is publicly traded on the NYSE under the ticker symbol TNET. If you’re passionate about innovation and making an impact on the large SMB market, come join us as we power our clients’ business success with extraordinary HR.

Don't meet every single requirement? Studies have shown that many potential applicants discourage themselves from applying to jobs unless they meet every single requirement. TriNet always strives to hire the most qualified candidate for a particular role, ensuring we deliver outstanding results for our small and medium-size customers. So if you're excited about this role but your past experience doesn't align perfectly with every single qualification in the job description, nobody’s perfect – and we encourage you to apply. You may just be the right candidate for this or other roles.

A Brief Overview

As a member of the Enterprise Data & Analytics team, the Lead Data Scientist is responsible for applying advanced analytical, statistical, and machine learning techniques to solve complex business problems. This role partners with cross-functional stakeholders to frame business questions, develop predictive and prescriptive models, and deliver insights that drive data-informed decision-making.

This individual will lead analytical solution design end-to-end, from problem framing and data exploration through modelling, validation, and deployment while ensuring results are interpretable, scalable, and aligned to business outcomes.

What You Will Do

Advanced Analytics & Modelling

• Develop, test, and deploy statistical models, machine learning models, and analytical frameworks.
• Apply techniques such as regression, classification, clustering, forecasting, and optimisation.
• Ensure models are explainable, reliable, and aligned with business objectives.

Business Problem Framing

• Partner with stakeholders to define analytical problems, hypotheses, and success criteria.
• Translate ambiguous business questions into well-structured analytical approaches.
• Identify key drivers, risks, and opportunities through data exploration and hypothesis testing.

Generative AI & Unstructured Data

• Ability to work with unstructured data and apply NLP and LLM‑based techniques to solve complex business problems.
• Hands‑on experience with Generative AI approaches, including prompt engineering, retrieval‑augmented generation (RAG), and evaluation of LLM outputs for accuracy, bias and business relevance.
• Strong understanding of how GenAI and LLM solutions are designed and integrated, and ability to partner with engineering teams to deliver scalable, governed AI solutions.

Data Exploration & Feature Engineering

• Perform exploratory data analysis to uncover patterns, trends, and anomalies.
• Engineer features from structured and unstructured data sources.
• Assess data quality, bias, and limitations in analytical outputs.

Model Validation & Operationalisation

• Validate model performance using appropriate metrics and testing approaches.
• Collaborate with data engineering and BI teams to operationalise models and insights.
• Monitor model performance over time and recalibrate as needed.

Communication & Storytelling

• Communicate findings, insights, and recommendations to technical and non-technical audiences.
• Translate complex analytical results into clear, actionable business narratives.
• Support decision-making with scenario analysis and impact assessments.

Continuous Improvement & Innovation

• Stay current with evolving data science methods, tools, and industry trends.
• Identify opportunities to apply advanced analytics and AI to new business problems.
• Promote analytical best practices across the organisation.

Education Qualifications

• Bachelor’s Degree in Data Science, Statistics, Mathematics, Computer Science, or related field
• Master’s Degree or PhD preferred

Experience Qualifications

• Typically 8+ years of experience in data science, advanced analytics, or applied statistics
• Hands-on experience with machine learning and statistical modelling techniques
• Experience working with large, complex datasets in an enterprise environment
• Experience partnering with business teams to drive measurable outcomes

Skills and Abilities

• Strong proficiency in Python, R, or similar analytical programming languages
• Strong foundation in statistics and machine learning
• Ability to frame and solve ambiguous business problems
• Experience balancing model sophistication with interpretability
• Strong critical thinking and problem-solving skills
• Ability to communicate complex concepts clearly and effectively
• Strong collaboration and stakeholder engagement skills
• Commitment to high professional and ethical standards

Licenses and Certifications (preferred)

• Microsoft Certified: Azure Data Scientist Associate (DP‑100) or equivalent 
• AWS Certified Machine Learning-Specialty or equivalent cloud ML certification
• Google Cloud Professional Machine Learning Engineer 
• Certified Analytics Professional (CAP/CAP‑X) by INFORMS preferred 
• TensorFlow or advanced machine learning certifications a plus

Travel Requirements

• Minimal Travel Required

Work Environment:

• Work in a clean, pleasant, and comfortable office work setting. The work environment characteristics described here are representative of those an employee encounters while performing the essential functions of this job. Reasonable accommodations may be made to enable persons with disabilities to perform the essential functions.
• This position is 100% in office.