Principal Data Scientist

Jobtailor; Acton (MA); On-site; USD 180,000 - 260,000; Full time

Job summary

Jobtailor is seeking a Senior Data Scientist to lead analytical strategy across the Data Science team and Enterprise Data & AI organization. You will set standards, oversee complex analyses, and drive production-ready models while guiding cross-functional initiatives.

You will mentor teams, shape roadmaps, and communicate findings to executives, aligning analyses with business value and risk considerations. This role requires deep statistical expertise and leadership.

Qualifications

• 10+ years of experience in data science, statistics, econometrics, operations research, applied mathematics, machine learning, or a related quantitative discipline.
• Expert-level knowledge of statistical methodology, including experimental design, causal inference, regression, forecasting, Bayesian methods, and machine learning
• Deep proficiency in Python and modern data science libraries, including pandas, NumPy, SciPy, statsmodels, and scikit-learn
• Extensive experience developing and validating predictive models and supporting their transition into production environments
• Demonstrated ability to translate ambiguous business challenges into clear analytical frameworks, decision criteria, and defensible quantitative solutions
• Proven ability to influence senior leaders and cross-functional teams through evidence, technical credibility, and clear communication
• Experience with Databricks or a comparable cloud-based data and analytics platform
• Publications, patents, conference presentations, open-source contributions, or other evidence of external or internal thought leadership
• Experience advising executive leaders on strategic decisions through quantitative evidence
• Experience working across geographically distributed, multidisciplinary teams

Responsibilities

• Define and evolve analytical methodologies, standards, and best practices across the Data Science team and broader Enterprise Data & AI organization
• Shape the analytical roadmap and prioritize investments based on business value, feasibility, decision risk, and data readiness
• Serve as the senior technical escalation point for complex statistical, experimental, modeling, and measurement questions
• Provide technical direction for major cross-functional, enterprise-wide initiatives
• Lead novel, high-complexity analyses supporting strategic and high-consequence decisions
• Establish standards for hypothesis testing, power analysis, confidence intervals, effect-size reporting, multiple-comparison control, sensitivity analysis, and uncertainty quantification
• Define causal inference approaches and enterprise experimentation frameworks
• Lead advanced time-series analysis, forecasting, anomaly detection, survival analysis, simulation, segmentation, and optimization
• Define standards for predictive and machine learning model design, validation, explainability, monitoring, and lifecycle management
• Provide technical oversight for critical predictive and machine learning models and guide their productionization
• Partner with senior leaders to frame ambiguous strategic challenges into analytical programs with explicit decisions, hypotheses, success measures, and value expectations
• Advise leaders on measurement strategy, experimentation, uncertainty, risk, and trade-offs
• Establish standards for reproducible analysis, code quality, peer review, documentation, validation, and release readiness
• Lead independent technical reviews of high-impact analyses and models
• Create reusable frameworks, libraries, templates, and reference implementations
• Mentor Data Scientists and Senior Data Scientists and raise technical standards through coaching, reviews, and learning sessions
• Influence hiring standards, interview practices, and technical assessment criteria
• Partner with Insights & Analytics, Analytics Engineering, Data Engineering, AI Engineering, MLOps, governance teams, domain leaders, and product owners
• Communicate methods, findings, limitations, and recommendations to executives, business stakeholders, technical peers, and governance audiences
• Lead or contribute to cross-functional delivery forums, technical reviews, and enterprise communities advancing responsible data science

Skills

Statistical Methodology

Predictive Modeling

Python Proficiency

Technical Leadership

Data Science Standards

Education

Master's Degree

PhD

Tools

Databricks

Job description

• Define and evolve analytical methodologies, standards, and best practices across the Data Science team and broader Enterprise Data & AI organization
• Shape the analytical roadmap and prioritize investments based on business value, feasibility, decision risk, and data readiness
• Serve as the senior technical escalation point for complex statistical, experimental, modeling, and measurement questions
• Provide technical direction for major cross-functional, enterprise-wide initiatives
• Lead novel, high-complexity analyses supporting strategic and high-consequence decisions
• Establish standards for hypothesis testing, power analysis, confidence intervals, effect-size reporting, multiple-comparison control, sensitivity analysis, and uncertainty quantification
• Define causal inference approaches and enterprise experimentation frameworks
• Lead advanced time-series analysis, forecasting, anomaly detection, survival analysis, simulation, segmentation, and optimization
• Define standards for predictive and machine learning model design, validation, explainability, monitoring, and lifecycle management
• Provide technical oversight for critical predictive and machine learning models and guide their productionization
• Partner with senior leaders to frame ambiguous strategic challenges into analytical programs with explicit decisions, hypotheses, success measures, and value expectations
• Advise leaders on measurement strategy, experimentation, uncertainty, risk, and trade-offs
• Establish standards for reproducible analysis, code quality, peer review, documentation, validation, and release readiness
• Lead independent technical reviews of high-impact analyses and models
• Create reusable frameworks, libraries, templates, and reference implementations
• Mentor Data Scientists and Senior Data Scientists and raise technical standards through coaching, reviews, and learning sessions
• Influence hiring standards, interview practices, and technical assessment criteria
• Partner with Insights & Analytics, Analytics Engineering, Data Engineering, AI Engineering, MLOps, governance teams, domain leaders, and product owners
• Communicate methods, findings, limitations, and recommendations to executives, business stakeholders, technical peers, and governance audiences
• Lead or contribute to cross-functional delivery forums, technical reviews, and enterprise communities advancing responsible data science

Requirements

• 10+ years of relevant professional experience in data science, statistics, econometrics, operations research, applied mathematics, machine learning, or a related quantitative discipline
• Demonstrated track record of providing technical leadership for complex, cross-functional analytical initiatives with material business impact
• Expert-level knowledge of statistical methodology, including experimental design, causal inference, regression, forecasting, Bayesian methods, and machine learning
• Deep proficiency in Python and modern data science libraries, including pandas, NumPy, SciPy, statsmodels, and scikit-learn
• Extensive experience developing and validating predictive models and supporting their transition into production environments
• Demonstrated ability to translate ambiguous business challenges into clear analytical frameworks, decision criteria, and defensible quantitative solutions
• Proven ability to influence senior leaders and cross-functional teams through evidence, technical credibility, and clear communication
• Strong experience conducting technical reviews, establishing analytical standards, and mentoring experienced practitioners
• Hands-on experience with Databricks or a comparable cloud-based data and analytics platform
• Exceptional written and verbal communication skills, including the ability to explain complex quantitative concepts to non-technical audiences
• Master's degree or PhD in statistics, mathematics, econometrics, operations research, computer science, engineering, or a related quantitative field (preferred)
• Experience in a regulated industry such as medical devices, healthcare, pharmaceuticals, life sciences, or financial services (preferred)
• Demonstrated depth in one or more areas such as experimentation, causal inference, forecasting, survival analysis, optimization, or advanced machine learning (preferred)
• Experience establishing analytical standards, governance practices, reusable frameworks, or enterprise data science capabilities (preferred)
• Publications, patents, conference presentations, open-source contributions, or other evidence of external or internal thought leadership (preferred)
• Experience advising executive leaders on strategic decisions through quantitative evidence (preferred)
• Experience working across geographically distributed, multidisciplinary teams (preferred)

Core Competencies

Expertise in statistical methodology, machine learning, and data science, with a strong focus on experimental design, causal inference, and predictive modeling. Proven ability to lead complex analytical initiatives and communicate findings effectively to diverse stakeholders.

Highest-signal resume keywords

• Statistical Methodology
• Predictive Modeling
• Python Proficiency
• Technical Leadership
• Data Science Standards

Hard Skills

• Experimental Design
• Causal Inference
• Regression Analysis
• Forecasting
• Bayesian Methods
• Machine Learning
• Time-Series Analysis
• Anomaly Detection
• Optimization
• Simulation

Soft Skills

• Clear Communication
• Mentoring
• Influencing Senior Leaders
• Technical Credibility
• Collaboration

Certifications & Qualifications

• Master's Degree
• PhD

Industry Keywords

• Data Science
• Statistics
• Econometrics
• Operations Research
• Healthcare
• Pharmaceuticals
• Financial Services
• Life Sciences

Tools & Technologies

• Python
• Pandas
• NumPy
• SciPy
• Statsmodels
• Scikit-learn
• Databricks