People Scientist
- Expanding a cutting-edge library of tests and tools.
- Designing bespoke activities and experiences for clients.
- Evaluating and refining AI-driven scoring algorithms and large language models (LLMs) to ensure fairness, accuracy, and transparency.
- Leveraging psychometric expertise to build reliable, valid, and impactful assessments.
- Developing tools that analyze candidate and job data to predict performance and potential with precision.
- Supporting clients in using assessment data to optimize their workforce strategies, from talent acquisition to development and retention.
- Leading original studies to explore emerging psychological and technological trends and sharing insights through publications, presentations, and client reports.
- Collaborating with regulatory bodies and industry leaders to establish new standards in ethical AI use and hiring practices.
- Equipping internal teams and clients with the knowledge and skills needed to understand and apply psychological and AI-driven insights effectively.
- Assess the statistical accuracy and reliability of LLMs used for automated scoring (e.g., structured grid methods; job-specific skills; multi-lingual proficiency tests - written and spoken).
- Compare and validate STT/TTS models and assess their downstream impact on candidate scores.
- Continuously identify and evaluate emerging LLM, STT, and TTS models to optimise scoring precision and efficiency.
- Evaluate and calibrate psychometric models (e.g., CTT, IRT, CFA) to ensure the scientific validity and comparability of AI-scored assessments across populations and test forms.
- Design research comparing AI-scored assessments with expert human judgments to ensure validity and alignment.
- Benchmark semantic and embedding models (e.g., BERT, GPT-4, MPNet, DeepSeek) for diverse assessment types.
- Develop hybrid scoring pipelines combining human oversight and AI-driven analytics.
- Detect and analyse potential biases in AI-generated or psychometric scores across demographic groups.
- Apply fairness and bias-mitigation techniques (e.g., reweighting, calibration, subgroup analysis) while maintaining model performance integrity.
- Contribute to internal fairness dashboards and compliance documentation, supporting transparent model governance.
- Continuously evaluate model generalisability and fairness to ensure all predictive algorithms adhere to ethical and scientific standards.
- Work with large-scale assessment and performance datasets to model relationships between candidate scores, job performance, and retention outcomes.
- Develop and test predictive models that estimate success probabilities or identify key behavioural and linguistic predictors of performance.
- Collaborate with data science, implementation and customer success teams to translate insights into actionable recommendations for clients and internal stakeholders.
- Investigate anomalies raised by clients or internal QA.
- Conduct diagnostic analyses and recommend evidence-based improvements.
- Explore fine-tuning, prompt-engineering, and evaluation methods to enhance model performance.
- Translate technical findings into actionable insights for non-technical stakeholders.
- Prepare and disseminate research through internal reports, publications, or conferences.
- Advanced degree (PhD/MSc) in Data Science, Machine Learning, Psychometrics, Computational Linguistics, or Psychology.
- Proven expertise in AI model evaluation, psychometric validation, and statistical analysis.
- Basic knowledge of psychometric modelling (e.g., IRT, CFA, CAT) and its application in assessment design and validation.
- Familiarity with LLMs and NLP techniques used for automated assessment and scoring.
- Experience applying fairness and bias testing methodologies in AI-driven decisions.
- Skilled in validation research ensuring reliability, construct validity, and practical relevance of assessments.
- Proficiency in Python or R and experience with statistical software (e.g., SPSS, Mplus, JASP) and cloud databases (e.g., BigQuery).
- Strong grounding in ethical AI, data governance, and compliance.
- Experienced in collaborating across teams (engineering, product, content) and communicating insights clearly to both scientific and business audiences.
- Skilled in data visualisation and research writing, with a track record of publications or applied studies.
- Stage 1 - Screening assessment (20 mins)
- Stage 2 - Hiring manager interview (45 min)
- Stage 3 - Power skill assessment with our AI agent (15 min)
- Stage 4 - Executive interview (45 min)
- Stage 5 - Deep-dive technical interview (60 min)
- Stage 6 - Interview with Co-founder (30 min)
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