Statistical Consulting — P6

STATIS.STATISTI51DA.P6

P6P6 — Principal Professionalhigh0.80approvedglobalv1

Provides expert statistical and biostatistical consultation across clinical research and scientific studies—study design, statistical analysis plans, analysis execution, interpretation, and regulatory submission support—and advises clients ranging from businesses to research institutions under a consulting-center model. Distinct from pure data engineering or data science by its grounding in inferential rigor (trial design, survival/Bayesian/mixed models, CDISC and regulatory standards) and consultative translation of statistical results for researchers, clients, and regulators.

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Level

P6 · P6 — Principal Professional · 12–18 yrs

Function · Focus

Statistical Consulting · Statistical Consulting

Market pay (median)

$192k ($151k–$244k)

Pay basis

function-anchored · wide bars

Focus — Statistical Consulting

Responsibilities by level

What this person actually does at each level on the professional track — escalating scope, not one generic blob. Your level is highlighted.

Performs basic analyses in SAS, R, or STATA under direction, executing supervised, execution-focused analytic work
Cleans and validates datasets—organizing, checking for inaccuracies, weighting, and structuring raw data to CDISC conventions prior to analysis
Prepares tables, charts, and visualizations (including in Excel and Tableau) to support senior statisticians
Supports senior statisticians with model development and contributes drafted sections to statistical analysis plans
Supports data analysis in clinical research or medical investigations following defined procedures

Independently leads statistical input into trial design for assigned studies, contributing sample-size and power calculations
Authors statistical analysis plans and contributes to protocol development
Responds to incoming consultation requests from researchers and study teams, advising on analytic approach and interpreting results
Plans day-to-day analytic work and leads or influences study-level projects, resolving routine analytic challenges proactively
Provides informal mentoring and functional guidance to less experienced statisticians

Leads study design and analysis on targeted studies and trials, selecting methods across complex variables (adaptive designs, group sequential methods, mixed models)
Owns development and quality review of statistical study documents; writes SAPs, statistical reports, and methodology sections of Clinical Study Reports
Provides statistical consultation to researchers and study teams across functional groups, including biomarker analysis and safety reporting; assists with protocols, investigator brochures, and regulatory responses
Peer-reviews SAPs written by others, validates core tables, listings, and figures programmed by others, and performs overall QC review before release
Leads and guides study teams with statistical direction, coordinating analytic activities across groups and serving as SME across multiple departments

Acts independently on broad and special statistical assignments, applying advanced methodology (Bayesian, survival, network meta-analysis, missing-data strategies) to strategic studies contributing to company objectives
Serves as lead statistician on targeted programs, providing statistical consultation that shapes analytic strategy and advising researchers and clients on method selection and trade-offs
Resolves ambiguous methodological problems where established approaches do not directly apply, ensuring validity, efficiency, and regulatory defensibility
Begins contributing to regulatory submissions, drafting and defending statistical methodology in responses to agency questions
Builds influential networks with researchers, clients, and cross-functional leaders, translating complex statistical results into actionable decisions

P6this profile

Serves as lead statistician for programs of studies and submissions, providing oversight across several trials simultaneously and contributing to strategic decisions
Oversees statistical activities supporting interactions with clients and regulatory agencies, and reviews and approves statistical methods sections of protocols and SAPs across a program portfolio
Develops and innovates statistical methodologies and contributes to regulatory submissions to guide drug development and approval
Participates in bid defense meetings and presentations to prospective clients, serving as the Statistics representative for cross-functional teams
Mentors junior and senior statisticians and provides critical statistical oversight across the program

Sets long-term statistical strategy across programs and anticipates emerging methodological and regulatory challenges
Develops novel statistical theories, models, and approaches to solve ambiguous, precedent-free problems with broad drug-development consequences
Influences regulatory submission strategy and engages regulators and industry leaders to advance accepted statistical practice
Provides high-level mentorship to senior and principal statisticians, building statistical capability across the organization
Operates with complete independence to define direction for statistical functions, projects, and industry initiatives

Level guidelines

The universal leveling rubric applied to this function — how scope, complexity, collaboration, and experience step up across levels.

Level	Knowledge & Application	Complexity & Problem Solving	Collaboration & Interaction	Typical Degree & Years
P2	Applies foundational statistical methods and programming (SAS, R, STATA) to conventional analytic tasks; data cleaning to CDISC structure, descriptive analyses, and table production within defined procedures.	Moderate; exercises judgment in familiar analytic contexts, escalating non-routine issues to senior statisticians.	Builds productive working relationships within the study team; supports senior statisticians and may mentor interns or graduate students.	2+ years with a BA, or MS/PhD with no experience.
P3	Applies a diverse range of statistical methods with moderate independence, evaluating identifiable factors to author SAPs, lead trial-design input, and field consultation requests on assigned studies.	Evaluates identifiable factors to resolve analytic challenges proactively across diverse study problems with day-to-day independence under milestone review.	Networks with senior researchers and statisticians; coordinates analytic activities and informally mentors junior staff.	5+ years (BA), 3 years (MA), or PhD without experience.
P4	Applies in-depth analysis of complex statistical variables—trial design, adaptive methods, biomarker/safety analysis, QC and validation, regulatory responses—with functional impact on study and program outcomes.	In-depth analysis of complex methodological variables; selects methods and resolves issues spanning trial design through reporting and peer review.	Coordinates across study and regulatory groups; provides consultation to researchers, serves as SME across departments, and may lead study teams.	8+ years, often with graduate education in statistics or biostatistics.
P5	Brings expert mastery of advanced methodology (Bayesian, survival, network meta-analysis, missing-data strategies) to broad and special assignments contributing to company objectives, including direct contribution to regulatory submissions.	Resolves ambiguous and intangible methodological problems with high independence where established approaches do not directly apply.	Builds influential networks across departments and with clients and regulators; acts as a recognized statistical authority and lead statistician on targeted programs.	12+ years with extensive biostatistical expertise.
P6	Applies principal-level statistical expertise to programs and submissions, innovating methodologies and guiding regulatory approval across multiple concurrent trials.	Visionary problem-solving across multiple concurrent trials, programs, and regulatory interactions with full independence; oversees portfolio-level methodological decisions.	Influences clients, regulators, and cross-functional teams; leads bid defense, represents Statistics, and mentors senior staff.	15+ years; principal statistical expert, often PhD with program leadership.
P7	Develops new statistical theories, models, and approaches that advance the field and shape company-wide and industry statistical practice.	Solves precedent-free, ambiguous problems with broad business and industry consequences; defines long-term methodological and regulatory roadmaps.	Networks with executives, regulators, and industry leaders; persuades and educates senior stakeholders and provides high-level mentorship without necessarily having direct reports.	20+ years, or equivalent recognition (often PhD with significant industry contributions, publications, or regulatory influence).

Skills

Focus-specific skills the role applies — the relevance layer beyond the occupational base.

Statistical analysis and interpretation: Analyze and interpret statistical data to identify significant differences in relationships and provide usable information.
Data preparation and validation: Organize, clean, check for inaccuracies, weight, and validate raw data prior to analysis.
Statistical method evaluation: Evaluate statistical methods to ensure validity, applicability, efficiency, and accuracy.
Study design: Design studies including sample size and power calculations, adaptive trial designs, and group sequential methods.
Regression and modeling: Apply generalized linear models (logistic, probit, Poisson) and linear and non-linear mixed models.
Survival analysis: Analyze time-to-event data including time-varying survival models.
Meta-analysis and evidence synthesis: Conduct pairwise meta-analysis, network meta-analyses, MAIC/PAIC, and individual patient-data analyses.
Bayesian methods: Apply Bayesian statistical approaches to analysis and inference.
Longitudinal and mixed models: Analyze repeated-measures and longitudinal data using mixed models.
Missing-data and multiple-testing strategies: Apply methods to handle missing data and adjust for multiple comparisons.
Simulation and interim analysis: Run statistical simulations and perform interim and group sequential analyses.
Regulatory compliance and submissions: Ensure research adheres to regulatory and scientific standards and contribute to regulatory submissions.
CDISC data standards: Apply CDISC clinical data standards to data structure and submission.
Communication and translation: Translate technical statistical results into insights for clients and stakeholders to support data-driven decisions.
Client consultation: Advise and collaborate with clients ranging from businesses to research institutions.
SAS: Uses this tool/technology effectively during the delivery of day-to-day tasks.
R: Uses this tool/technology effectively during the delivery of day-to-day tasks.
Python: Uses this tool/technology effectively during the delivery of day-to-day tasks.
IBM SPSS Statistics: Uses this tool/technology effectively during the delivery of day-to-day tasks.
Minitab: Uses this tool/technology effectively during the delivery of day-to-day tasks.
MATLAB: Uses this tool/technology effectively during the delivery of day-to-day tasks.
STATA: Uses this tool/technology effectively during the delivery of day-to-day tasks.
Tableau: Uses this tool/technology effectively during the delivery of day-to-day tasks.
QlikView: Uses this tool/technology effectively during the delivery of day-to-day tasks.
Apache Spark: Uses this tool/technology effectively during the delivery of day-to-day tasks.
Apache Hadoop: Uses this tool/technology effectively during the delivery of day-to-day tasks.
Teradata: Uses this tool/technology effectively during the delivery of day-to-day tasks.
Amazon Redshift: Uses this tool/technology effectively during the delivery of day-to-day tasks.
AWS: Uses this tool/technology effectively during the delivery of day-to-day tasks.
IBM DB2: Uses this tool/technology effectively during the delivery of day-to-day tasks.
Microsoft SQL Server: Uses this tool/technology effectively during the delivery of day-to-day tasks.
Oracle: Uses this tool/technology effectively during the delivery of day-to-day tasks.
Microsoft Access: Uses this tool/technology effectively during the delivery of day-to-day tasks.
SQL: Uses this tool/technology effectively during the delivery of day-to-day tasks.
C++: Uses this tool/technology effectively during the delivery of day-to-day tasks.
Java: Uses this tool/technology effectively during the delivery of day-to-day tasks.
C#: Uses this tool/technology effectively during the delivery of day-to-day tasks.
Perl: Uses this tool/technology effectively during the delivery of day-to-day tasks.
Bash: Uses this tool/technology effectively during the delivery of day-to-day tasks.
Linux: Uses this tool/technology effectively during the delivery of day-to-day tasks.
Shell script: Uses this tool/technology effectively during the delivery of day-to-day tasks.
UNIX: Uses this tool/technology effectively during the delivery of day-to-day tasks.
Excel: Uses this tool/technology effectively during the delivery of day-to-day tasks.

Provenance

The evidence base behind this profile — every layer is sourced; quality is scored by an adversarial review panel (1–5; passes at ≥4 on the minimum dimension).

Level differentiation4.5Focus specificity4.5Concreteness4.5Factual accuracy4.0Real-world coverage4.5

11 sources

Level — P6 — Principal Professional

Top individual contributor; recognized authority with strategic impact, equivalent to a low executive level

Scope: Organization-wide architecture and the hardest problems
Autonomy: Defines direction; minimal oversight
Complexity: Strategic, open-ended problems shaping the technical future
Impact: Organization-wide
Decision rights: Sets technical strategy for a major area
Leadership: Recognized authority; multiplies many teams
Typical experience: 12–18 yrs

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