[Your Full Name]
[Your Phone Number] | [Your Email] | [City, State]
LinkedIn: [Your LinkedIn URL] | GitHub: [Your GitHub URL] | Google Scholar: [Profile]

PROFESSIONAL SUMMARY
Data Scientist transitioning from academia with [X] years research experience and [Y] publications. Strong foundation in statistical modeling, experimental design, and Python programming. Eager to apply advanced quantitative skills to solve real-world business problems and drive data-driven decisions.

TECHNICAL SKILLS
Programming: Python, R, SQL, MATLAB
Machine Learning: Scikit-learn, TensorFlow, PyTorch, Statistical Modeling
Research Methods: Experimental Design, Hypothesis Testing, Statistical Analysis, Causal Inference
Tools: Jupyter, Git, LaTeX, SPSS
Visualization: Matplotlib, Seaborn, ggplot2, Tableau
Transferable Skills: Problem Solving, Scientific Communication, Project Management

RESEARCH EXPERIENCE

Postdoctoral Researcher / PhD Candidate
[University Name], [Department], [City, State]
[Start Date] – [End Date]
• Conducted research in [field] using statistical modeling and machine learning techniques
• Analyzed datasets of [size] applying [specific methods] to answer [research questions]
• Published [number] papers in peer-reviewed journals with [X] citations
• Presented findings at [number] international conferences
• Mentored [number] graduate/undergraduate students on research methods
• Secured $[amount] in research funding through grant applications

Graduate Research Assistant
[University Name], [City, State]
[Start Date] – [End Date]
• Developed computational models for [specific application]
• Collected and processed [type/size] of data
• Performed statistical analyses and hypothesis testing
• Collaborated with [X] researchers across [Y] institutions

INDUSTRY-RELEVANT PROJECTS

[Project Name]: Predictive Modeling
• Problem: [Research question framed as business problem]
• Approach: Built [model type] using [techniques]
• Results: Achieved [metric] with practical applications in [industry/domain]
• Technologies: Python, Scikit-learn, Pandas

[Project Name]: Data Analysis Pipeline
• Automated data processing workflow handling [volume] of data
• Reduced analysis time from [X] days to [Y] hours
• Created visualization dashboard for non-technical stakeholders
• Technologies: Python, SQL, Tableau

[Consulting or Industry Collaboration]
• Partnered with [company/organization] on [project]
• Applied academic methods to solve [business problem]
• Delivered [outcome or recommendation]

PUBLICATIONS

• [Author List]. "[Paper Title]." [Journal Name], [Year]. [Citations if significant]
• [Author List]. "[Paper Title]." [Conference Name], [Year].
• [Author List]. "[Paper Title]." [Journal Name], [Year].

EDUCATION

Ph.D., [Field]
[University Name], [City, State]
[Year]
Dissertation: "[Title]"

[Master's Degree], [Field]
[University Name], [Year]

[Bachelor's Degree], [Field]
[University Name], [Year]

PROFESSIONAL DEVELOPMENT

Industry Transition Training
• [Data Science Bootcamp or Course], [Platform], [Year]
• [Business Analytics Course], [Platform], [Year]
• [Industry Workshops attended]

TRANSFERABLE SKILLS FROM ACADEMIA

• Analytical Thinking: [X] years solving complex problems through systematic analysis
• Communication: Explained complex concepts to diverse audiences through [presentations, teaching]
• Project Management: Managed multiple research projects with competing deadlines
• Collaboration: Worked with cross-functional teams across institutions
• Learning Agility: Quickly mastered new tools and methodologies for research needs