Staff Data Scientist

Us RemoteRemoteFull Timelead

Data ScienceData ScientistData & AI

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Quick Summary

Overview

15Five is the AI-powered performance management platform built for business impact. 15Five’s AI-powered all-in-one people management system is easy to use, delivers effortless insights,

Technical Tools

Data ScienceData ScientistData & AI

15Five is the AI-powered performance management platform built for business impact. 15Five’s AI-powered all-in-one people management system is easy to use, delivers effortless insights, and enables managers to lead with impact so that companies and their people can thrive. Within the flow of work, HR leaders are empowered with data-driven insights and recommendations while managers are transformed into change-makers, accelerating engagement, performance, and retention. 15Five combines generative AI, custom analytics, and human-centered principles within a complete platform, including 360° performance reviews, engagement surveys, goal tracking, manager coaching and training, and ongoing feedback tools like guided 1-on-1s and check-ins.

A career at 15Five is the chance to have high impact in a fast paced, remote startup environment that operates with focus, urgency, and accountability. Every role and every hire meaningfully impacts our trajectory.

We’re a high-performance, high-purpose team that gives you the chance to Build the Future of Work. We run on High Standards and High Support — holding a high bar for results, ownership, clarity, and disciplined execution, while investing in manager enablement, skill development, and whole-person care. We are committed to a No B.S. environment, meaning we anchor ourselves in truth, course-correct in real time, and expect people to lean into direct conversations. We are focused on ensuring every 15Fiver can Work in the Future and that starts with proactively using AI across all aspects of your role to increase leverage, speed, and impact.

If you're energized by a fast pace, high standards, and high accountability for yourself and others, you'll thrive here and we encourage you to apply.

We take pride in our position as an innovator in the Human Resources landscape. This wouldn’t be possible without our outstanding team of engineers that apply next-generation thinking to our software - their abilities allow us to continually elevate the user experience and push leading performance-management technology into the global market.

As an experienced Staff Data Scientist, you will play a critical role driving the discovery, creation, and integration of AL/ML models into our performance-management platform at 15Five. You will combine statistical methods with engineering best practices to help meet our business needs. We will rely on your statistical analysis skills, as well as insight for collecting, filtering, and preparing meaningful training data for AI/ML projects. You will be instrumental in modeling HR data and building rich structured context to power our knowledge base as well as AI agents engaging with our customers. You will work closely and collaborate with software and data engineers and guide them on operationalizing your work. You will be a self-starter, and build from the ground up leveraging your experience.

Applied data science

Statistical analysis

Feature engineering

Supervised/Unsupervised model training & delivery

Data analysis and exploration: Conduct in-depth analysis of large structured and unstructured datasets to uncover patterns, insights, and signals that inform AI models, knowledge representations, and product decisions.

Statistical modeling: Develop and apply statistical methods to identify patterns, trends, and correlations in HR performance, engagement, and behavioral data, supporting evidence-based insights and model development.

Feature engineering and knowledge modeling: Design and engineer features from diverse data sources (e.g., reviews, surveys, behavioral signals, organizational data) to power machine learning models and construct structured AI context layers and knowledge representations used by intelligent systems and agents.

Training data development: Lead the creation and curation of high-quality labeled datasets, including annotation frameworks, evaluation datasets, and ground truth benchmarks that support model training and validation.

Machine learning model development: Design, develop, and train machine learning models (supervised, unsupervised, and representation-learning approaches) for use cases that enhance customer outcomes and enable intelligent product capabilities.

Context layer and AI system support: Develop data structures, embeddings, and context-enrichment pipelines that enable AI agents to retrieve relevant organizational knowledge, behavioral insights, and product context.

Model deployment and integration: Partner with engineering teams to operationalize models in production environments, ensuring scalability, reliability, and alignment with product architecture.

Model evaluation and iteration: Design evaluation frameworks and continuously monitor model performance, iterating on models, data pipelines, and features based on experimentation, feedback, and evolving business needs.

Cross-functional collaboration: Work closely with product, engineering, and design teams to translate business problems into data science solutions and ensure models deliver measurable value.

Documentation and knowledge sharing: Maintain clear documentation for datasets, modeling approaches, evaluation frameworks, and data pipelines to ensure reproducibility, transparency, and knowledge transfer across teams.

AI evaluation and benchmarking: Design evaluation datasets and benchmarking frameworks to measure AI agent performance, including retrieval quality, reasoning accuracy, and contextual relevance.

Data governance and responsible AI: Ensure that data used in models and AI systems meets privacy, security, and ethical standards, particularly when working with sensitive employee performance and engagement data.

Education: BS or higher in Computer Science, Statistics, Mathematics, Physics, or a related quantitative field, or equivalent practical experience.

Industry experience: 7+ years of experience in data science, machine learning, or applied statistics in a production environment.

Statistical expertise: Strong foundation in statistical methods and the ability to apply them to large, complex datasets to identify patterns, trends, and actionable insights.

Machine learning experience: Demonstrated experience developing, evaluating, and deploying machine learning models that drive measurable business impact. Experience with large language models (LLMs), embeddings, or AI-driven systems is a strong plus.

Programming proficiency: Strong programming skills in Python and SQL, with experience building data processing pipelines, training models, and analyzing large datasets.

Data infrastructure experience: Experience working with modern data platforms including data warehouses, relational databases, and distributed data processing systems.

MLOps and production systems: Familiarity with MLOps practices and tools for model training, versioning, deployment, monitoring, and lifecycle management.

Software engineering practices: Understanding of modern software development workflows, including version control (Git), CI/CD pipelines, code review, testing practices, and agile development methodologies.

Communication and collaboration: Strong ability to communicate complex technical concepts to non-technical stakeholders and collaborate effectively with product, engineering, and cross-functional teams.