• Gather, clean, and preprocess data from various sources to ensure its quality and usability for analysis. • Analyze data using statistical methods and tools to identify trends, patterns, and insights that can inform business decisions. • Develop predictive and prescriptive models to forecast future outcomes and optimize business processes. • Create reports and visualizations to effectively communicate findings to stakeholders and support decision-making. • Work closely with business units to understand their data needs and provide actionable insights that drive strategic initiatives. • Monitor and evaluate the performance of implemented models and strategies, adjusting as necessary to improve outcomes. • Conduct ad hoc analyses to support various business needs and answer specific questions as they arise. • Stay updated with the latest trends and technologies in data analytics and continuously improve analytical methods and processes. • Other duties as assigned.

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. Staff Data Scientist Role 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. Areas of Focus - Applied data science - Statistical analysis - Feature engineering - Supervised/Unsupervised model training & delivery Responsibilities - 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. Required Experience/Competencies/Attributes - 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. Compensation & Benefits Annual Base Pay Range, US Full-Time Staff Data Scientist: $132,500 - $170,500/year Range may vary based on candidate's home location and proximity to different US compensation zones. This role's Total Rewards package includes participation in an annual discretionary company bonus program, equity awards, and comprehensive benefit and wellbeing offerings. We offer amazing benefits that align with our values and culture at 15Five. Your Health & Wellbeing Medical, dental, vision with 15Five cost subsidy Employer paid Short-Term, Long-Term Disability, and Term LifeFamily planning support with enhanced medical plans and consultation programs Inclusive Benefits Stipend Access to mental health and wellness resources Your Life Flexible Time Off Program Generous paid leave for new parents Military leave Paid Company Holidays Sabbatical Program Your Future 401K with 4% Matching after 6 months Your Work Life Remote work Work with experts in leadership, culture, and personal development For more information see: Our Mission, Vision, & Values -https://www.15five.com/about Our People and Culture -https://www.15five.com/about/careers

• Focus on understanding existing models, assessing their performance, selecting optimal architectures, and fine-tuning them to meet specific domain and business needs—including retrieval-augmented generation (RAG) based applications. • Collaborate closely with data engineering, product, and domain teams to translate real-world research challenges into scalable, model-driven solutions that accelerate Advarra’s vision of a digitally connected research data and technology fabric. • Optimize and fine-tune large language models (LLMs) and domain-specific variants using proprietary datasets to achieve precision and recall targets that drive differentiated customer value. • Evaluate model performance across key metrics and benchmarks, identifying strengths, weaknesses, and opportunities for improvement across predictive, generative, and retrieval-augmented tasks. • Implement and operationalize LLM-based and retrieval-augmented (RAG) systems that enhance Braid-powered products such as Study Design and Site Feasibility. • Collaborate with data engineering to ensure scalable, efficient model training, evaluation, and deployment pipelines using Databricks, MLflow, and Delta Lake. • Assess and select models—open-source or proprietary—that best align with domain-specific requirements and Advarra’s regulated research environment. • Partner with clinical and operational experts to translate research and trial challenges into measurable model evaluation frameworks and optimization strategies. • Conduct model interpretability and bias analyses to ensure fairness, transparency, and compliance with governance standards. • Document methodologies and validation results to support internal governance, reproducibility, and audit readiness. • Contribute to reusable fine-tuning workflows, evaluation frameworks, and model monitoring pipelines within the Braid AI stack. • Stay at the forefront of advancements in LLM optimization, retrieval augmentation, and multi-modal learning, applying new methods that improve scalability, explainability, and cost efficiency

Role Description - Design and implement data models for identity graph analysis using Python and PySpark. - Develop and optimize algorithms to enhance the accuracy of identity verification processes. - Collaborate with cross-functional teams to integrate data solutions into existing systems. - Utilize Vector Store and NeptuneDB to manage and analyze large datasets effectively. - Create visualizations and reports that communicate insights to stakeholders. - Conduct experiments and A/B testing to evaluate the performance of machine learning models. - Stay updated on industry trends and emerging technologies to continuously improve data strategies. Qualifications - 5+ years of experience in data science or machine learning engineering roles. - Proficiency in Python and PySpark for data manipulation and analysis. - Experience with Vector Store and graph databases, particularly NeptuneDB. - Strong understanding of SQL for data querying and management. - Familiarity with OpenCypher and GraphFrames for graph data processing. Requirements - Experience with Databricks for collaborative data engineering and analytics. - Knowledge of cybersecurity principles and practices related to identity verification. - Familiarity with machine learning frameworks and libraries. Benefits - The base salary for this role ranges from $170K to $205K, accompanied by competitive equity options. - Join a dynamic team that values innovation and offers opportunities for professional growth and development. Company Description We're representing a leading provider of digital identity verification and fraud solutions. This company is at the forefront of leveraging advanced technology to ensure security and trust in digital transactions across various industries. With a commitment to innovation and excellence, they are poised to make a significant impact in the realm of identity management.