Staff Data Scientist

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.