Note: The job is a remote job and is open to candidates in USA. HR Acuity is a leading provider of employee relations case management and investigation software. They are seeking a Staff Data Scientist to develop a comprehensive understanding of their data landscape, build analytical models, and create customer-facing data products that help organizations understand patterns in their employee relations programs.
Responsibilities
- Develop and continuously deepen a comprehensive understanding of HR Acuity's full data landscape, including case types, taxonomies, workflows, and customer segments, grounded in the real-world employee relations problems the data represents
- Assess what makes HR Acuity's dataset competitively distinctive, identify gaps or additions that would strengthen its analytical value, and proactively surface opportunities that extend beyond the current product roadmap
- Build rigorous documentation, lineage, and data quality baselines for HR Acuity's ER dataset, establishing the foundation the analytics product needs to grow on
- Design and build statistical models and machine learning solutions that surface trends, risk signals, and benchmarks from HR Acuity’s employee relations case data, with rigorous attention to bias detection and mitigation given the sensitivity of the underlying data
- Apply NLP and text analytics techniques to extract structure, patterns, and signals from HR Acuity’s unstructured case data, including case notes, investigation narratives, and incident descriptions; this includes building and improving anonymization pipelines for sensitive unstructured content, with a focus on preserving analytical value while protecting identity
- Own and elevate HR Acuity’s customer-facing analytics and benchmarking capabilities: deepen their statistical rigor, expand the insights they deliver, and ensure they reflect the full analytical depth of the underlying dataset; this is the current core product, and making it more statistically meaningful and defensible is a primary responsibility of this role
- Design and execute experiments to measure the impact of product and analytical changes, applying rigorous statistical methods, including power analysis and significance testing, to feature evaluation and continuous improvement
- Collaborate with Data Engineering to ensure pipelines, schemas, and data models support analytical needs and maintain data quality
- Partner closely with Product and Engineering to define metrics, evaluate feature performance, and translate analytical findings into product decisions
- Translate complex analytical findings into clear, compelling narratives for non-technical stakeholders, including customers and internal leadership
- Apply responsible data practices throughout all work, prioritizing privacy, fairness, and ethical handling of sensitive employee information
- Define and maintain the statistical evaluation framework for AI and ML features, including golden datasets, quality metrics, and release gates for data quality
- Contribute to technical approach decisions for AI features, bringing data-driven judgment on when classical ML, statistical models, or rules-based solutions are more appropriate than LLM-based approaches
- Serve as the statistical and data quality voice on LLM-powered features, evaluating whether AI-generated insights, summaries, and recommendations are statistically sound, appropriately calibrated, and meaningful for customers
- Review prompt designs and model outputs for accuracy, bias, and statistical validity, partnering with engineers to iterate before and after release
- Monitor model and data quality over time, identifying and addressing degradation, drift, or unexpected changes in underlying distributions
- Establish data science standards, documentation practices, and reproducibility norms that scale as the team grows
- As the data science function grows, provide mentorship and technical guidance to junior practitioners, contributing to a culture of rigor and continuous learning
Skills
- Bachelor's or Master's degree in Statistics, Data Science, Computer Science, or a related quantitative field — or equivalent practical experience
- 6+ years of experience in data science, applied statistics, or machine learning
- Proven track record building and deploying predictive models in production environments
- Comfortable establishing model lifecycle practices in greenfield environments, including experiment tracking, versioning, and drift monitoring
- Strong Python skills across the data science ecosystem (pandas, scikit-learn, statsmodels, scipy, etc.)
- Solid SQL command with experience working with relational databases at scale
- Experience with Snowflake and cloud-based ELT patterns
- Demonstrated experience with unstructured text data, including NER, PII detection and anonymization pipelines, text classification, topic modeling, entity extraction, and embeddings
- Proficiency in experiment design and A/B testing methodology, including power analysis, significance testing, and translating results into product decisions
- Demonstrated ability to translate complex statistical concepts into clear, actionable narratives for varied audiences, in both written and visual formats
- Azure experience preferred, particularly for model pipelines and container-based workloads (e.g., Kubernetes)
- Experience building or maintaining systems that consistently replace identifying information across large document corpora is strongly preferred
- Experience evaluating LLM-powered features in production, including assessing AI-generated outputs for accuracy, calibration, and bias
- Familiarity with prompt review, golden dataset design, or model quality monitoring is strongly preferred
- Experience working with sensitive or compliance-adjacent data (HR, healthcare, legal, or financial) is strongly preferred
- Familiarity with privacy-preserving statistical methods for aggregate analytics
- Familiarity with statistical disclosure control techniques such as minimum cohort thresholds, suppression rules, and aggregation standards for sensitive data
Benefits
- Discretionary annual bonus
- Health and wellness benefits
- 401(k) retirement plan
- Life and disability insurance coverages
- Comprehensive medical, dental and vision plans
- FSA or HSA options
- 401K plan that matches your contributions
- Paid leave for various life events, such as sickness, disability, or parenthood
- #Allin Shares Program
- Company paid holidays
- Birthday day off
- Closed 4th of July week and December holiday week
- 8 hours of volunteer time
- Half day summer Fridays* and half day first Fridays* to catch up on work, personal development, or an afternoon off
- Unlimited PTO policy
- Earn extra cash by referring qualified candidates to join our team
- Access professional and personal support through our employee assistance program
- Work from anywhere with our remote work environment that fosters collaboration and creativity. *
- Join a fun and energetic team that values your suggestions and new ideas
- Receive a competitive salary and meaningful opportunities for growth
- Onboarding: Learn the basics of your role, the company culture, and the expectations from your manager and team. Get familiar with the tools, systems, and processes that you will use in your daily work. Receive feedback and guidance from your mentor and peers.
- Manager training: Develop the skills and competencies to lead, motivate, and empower your team. Learn how to communicate effectively, delegate tasks, set goals, provide feedback, and resolve conflicts. Enhance your emotional intelligence, coaching, and mentoring abilities.
- Leadership training: Grow your leadership potential and influence within the organization. Learn how to inspire and align others with the company vision, mission, and values. Strengthen your strategic thinking, decision making, and problem-solving skills. Expand your network and collaboration with other leaders across functions and levels.
- Industry training: Stay updated on the latest trends, best practices, and innovations in the Employee Relations industry. Gain insights from experts and thought leaders in the field. Apply your learning to improve your performance, quality, and efficiency.
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