We are seeking a Research Scientist to join our Data Analytics team. Data Analytics is an exciting and growing function, bringing together existing internal expertise with targeted external hiring. Our mission is to turn proprietary data into industry-leading insights on leadership—both to equip our client-facing teams and to generate high-impact intellectual capital.
We are a small, collaborative, and intellectually curious team that works closely with colleagues across Technology, Marketing, and the firm’s client-facing practices. This role offers a unique opportunity to operate at the intersection of advanced statistical research, real-world leadership impact, and client engagement, within a firm with unparalleled access to senior decision-makers globally.
This role is designed for someone with a strong interest in statistics, applied research, leadership, and business performance, and—critically—the ability to think rigorously about methodological approach. The Research Scientist is expected to frame researchable questions, develop and test hypotheses, and select and apply appropriate statistical approaches to address complex, business-critical leadership and governance questions. This includes determining how a question should be answered—not only what to analyze—by drawing on approaches such as regression frameworks, quasi-experimental designs, survival or event-history analysis, latent-variable models, or Bayesian methods, depending on the research objective and data structure. In addition to delivering rigorous research, the role requires the ability to translate analytical work into tangible client impact—through advisory support, live client interactions, analytics demonstrations, and proof-of-value work that helps position analytics as a differentiating capability. We explore questions such as: what characteristics make some leaders more successful than others? How do contextual conditions shape leadership risk and performance over time? How do culture, governance, and leadership interact to influence organizational outcomes?
In this role, your primary focus will be to:
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Design and support hypothesis-driven statistical research, including the formulation of testable hypotheses, definition of identification strategies, and critical evaluation of assumptions (e.g., causal vs. non-causal inference).
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Apply advanced statistical methodologies—including regression and generalized linear models, quasi-experimental methods (e.g., matching, difference-in-differences), survival and time-to-event analysis, latent-variable approaches, and Bayesian frameworks—to guide model selection, validation, robustness testing, and interpretation.
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Translate analytical findings into clear, executive-ready narratives, producing research memos, client-facing materials, and thought-leadership content that connect methodology and evidence to insight.
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Partner closely with the Lead Data Scientist to design, execute, validate, and document rigorous quantitative analyses aligned with high research standards.
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Actively participate in client-facing analytical work, supporting consultants in live engagements where data and insight strengthen leadership advisory.
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Deliver or co-deliver analytics demonstrations, showing how data and analytical tools can address client needs. Design and support Proofs of Concept (PoCs) and Proof-of-Value exercises, tailored to client-specific challenges and strategic questions.
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Help translate analytical capabilities into compelling value propositions, supporting early-stage client conversations and contributing to the origination of analytics-related opportunities.
LOCATION
Like the rest of the firm, the Data Analytics team works globally, with professionals located in Amsterdam and in the United States. Our strong preference is for a colleague to join our Amsterdam office.
KEY RELATIONSHIPS
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Reports to: Lead Data Scientist, Data Analytics (Amsterdam)
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Other key relationships:
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Head of Data Analytics
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Data Analytics team members/colleagues
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Industry/Function Practice Directors
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Marketing and other internal stakeholders (Operations, Finance, etc.)
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Data Engineering / Technology partners (as needed for research enablement)
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Client-facing Consultants and Practice Teams
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Business development / proposal stakeholders, as relevant to analytics-led opportunities
KEY RESPONSABILITIES
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Research Design and Methodological Rigor
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Support the design and execution of proprietary research projects on leadership, governance, culture, and performance.
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Apply appropriate statistical methodologies under guidance (e.g., regression frameworks, causal and quasi-experimental approaches, time-to-event/survival analysis, Bayesian approaches, latent-structure methods).
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Contribute to the development, documentation, and stress-testing of internal analytical frameworks, metrics, and indices.
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Conduct diagnostics, robustness checks, and sensitivity analyses to ensure analytical credibility.
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Insight Generation and Executive Storytelling
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Translate statistical findings into clear narratives suitable for senior, non-technical audiences.
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Draft or co-author internal research memos, client-facing exhibits, and thought-leadership content (e.g., articles, briefing notes, presentation storylines).
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Support the synthesis of results into executive summaries and high-stakes presentation materials.
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Client-Facing Analytics & Pre-Sales Contribution
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Support client engagements where analytics enhances leadership dialogue.
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Deliver or contribute to analytics demonstrations, showcasing capabilities and insights.
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Design and support Proofs of Concept (PoCs) and Proof-of-Value exercises addressing client-specific challenges.
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Partner with consultants to tailor analytical outputs to client contexts and decision needs.
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Contribute to the development of repeatable analytics offerings and scalable solutions.
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Support revenue generation efforts, helping position analytics in opportunity development while not owning commercial targets directly.
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Data Usage (Non‑Engineering Focus)
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Work with curated datasets extracted from the firm’s cloud-based data platforms and research repositories.
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Identify data limitations, assumptions, and risks relevant to statistical analysis; escalate quality issues where needed.
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Collaborate with data quality and engineering colleagues while not owning platform development, pipelines, or production systems.
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Analytical Tooling and Libraries
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Conduct analysis primarily in Python/SPSS, within a research‑oriented statistical environment that supports exploratory, inferential, and hypothesis‑driven work.
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Apply tooling appropriate for data manipulation, econometric and regression‑based modeling, quasi‑experimental analysis, survival and event‑history modeling, distributional and diagnostic analysis, and selected unsupervised or latent‑structure methods.
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Produce clear exploratory and presentation‑ready visualizations to support interpretation and executive‑level storytelling.
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Emphasize reproducible, well‑documented workflows, aligned with academic and high‑end applied research standards.
IDEAL EDUCATION AND EXPERIENCE
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Bachelor’s degree in Statistics, Economics, Mathematics, Data Science, Quantitative Social Science, or a related field; an advanced degree (Master’s or PhD) is strongly preferred.
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Approximately 2–5 years of experience in an applied research setting (consulting, academia, think tank, policy institute).
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Demonstrated ability to apply statistical reasoning to complex, imperfect real-world data.
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Proven writing ability, including research papers, policy reports, analytical publications, or client-facing research.
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Experience collaborating with non-technical stakeholders and translating analysis into actionable insight is a strong plus.
TECHNICAL SKILLS
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Statistical Analysis
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Strong foundation in parametric and non‑parametric statistical inference and modeling, including regression and generalized linear models, quasi‑experimental approaches for causal reasoning (e.g., propensity score methods, difference‑in‑differences), survival and time‑to‑event analysis, and familiarity with hierarchical or Bayesian thinking where appropriate.
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Exposure to unsupervised or latent‑structure techniques (e.g., clustering, factor models, PCA) to identify underlying patterns or archetypes in leadership data.
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Programming and Reproducible Research
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Proficiency in Python for analysis, reproducible workflows, and data visualization.
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Comfort working with structured datasets derived from SQL-based environments (SQL proficiency is helpful but not mandatory).
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Communication of Results
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Ability to present results clearly through concise exhibits, tables, and narrative summaries.
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Familiarity with visualization tools (e.g., Tableau) is a plus but not required.
CRITICAL CAPABILITIES:
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Strong statistical and methodological expertise
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Exceptional ability to communicate complex insights clearly to non-technical audiences
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Client-facing confidence, including presenting analytical work in live settings
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Commercial awareness, with the ability to link analytics to client value and opportunity creation
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Strong consultative mindset bridging research and application
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High intellectual rigor and attention to detail
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Self-motivated, collaborative, and able to operate in a fast-paced environment
