Data Scientist / Analyst

Written By Keandra Wilson

 1. OVERVIEW

Data scientists use statistical methods, machine learning, and computational tools to extract insights from large datasets. They clean and process raw data, build predictive models, and communicate findings through data visualizations and reports. Their work helps companies and organizations make informed decisions in fields ranging from marketing and healthcare to finance, logistics, and product development.

  • 2024 Median Pay: $112,590/year ($54.13/hour)

  • Job Growth (2023–2033): +36% (much faster than average)

  • Typical Entry-Level Education: Bachelor’s degree in data science, statistics, computer science, or related field

2. ROLE BREAKDOWN BY LEVEL

ENTRY LEVEL

Job Titles: Junior Data Scientist, Data Analyst, Machine Learning Associate
Education: Bachelor’s degree in Data Science, Statistics, Mathematics, or Computer Science
Experience: 0–2 years
Certifications (Recommended):

  • Google Data Analytics Certificate

  • IBM Data Science Professional Certificate

  • Microsoft Certified: Azure Data Scientist Associate

Core Duties:

  • Clean and preprocess raw data

  • Perform exploratory data analysis (EDA)

  • Build basic statistical models and run hypothesis tests

  • Create dashboards and simple data visualizations

  • Collaborate with engineers and business analysts to deliver insights

Salary Range: $70,000–$95,000

MID LEVEL

Job Titles: Data Scientist, Applied Scientist, Machine Learning Engineer, BI Developer
Education: Bachelor’s or Master’s degree
Experience: 3–6 years
Certifications (Preferred):

  • TensorFlow Developer Certificate

  • SAS Certified Advanced Analytics Professional

  • AWS Certified Machine Learning – Specialty

Core Duties:

  • Develop predictive models using machine learning algorithms

  • Lead A/B testing and advanced experimentation

  • Build and deploy pipelines for data ingestion and model retraining

  • Mentor junior staff and conduct peer code reviews

  • Present findings to cross-functional teams and senior stakeholders

Salary Range: $95,000–$135,000

SENIOR LEVEL

Job Titles: Senior Data Scientist, Lead Data Scientist, Data Science Manager, Head of Data Science
Education: Master’s or PhD in Data Science, Statistics, AI, or Applied Math
Experience: 7–12+ years
Certifications (Valued):

  • Certified Analytics Professional (INFORMS)

  • Data Science Leadership Certificate (Berkeley or MIT)

  • Professional Scrum Product Owner – Data Focus

Core Duties:

  • Define data strategy and analytics roadmap

  • Lead large-scale model deployment and experimentation

  • Manage data science teams and mentor staff

  • Interface with C-suite and advise on data-driven initiatives

  • Ensure compliance with data ethics, security, and model fairness

Salary Range: $135,000–$200,000+

3. HOW TO BECOME ONE

  • Minimum Education: Bachelor’s in data science or related field

  • Preferred: Master’s in data science, analytics, or AI

  • Training: Internships, Kaggle competitions, bootcamps (General Assembly, Springboard, Flatiron School)

  • Certifications: Google Data Analytics, IBM Data Science, AWS Machine Learning, CAP

  • Soft Skills: Communication, curiosity, critical thinking, business acumen

4. SKILLS & TOOLS

Core Skills:

  • Data cleaning & transformation (Pandas, SQL, Excel)

  • Statistical analysis & hypothesis testing

  • Model development (supervised, unsupervised, NLP, deep learning)

  • Data storytelling & visualization

  • Agile or SCRUM project methodologies

Tools:

  • Programming: Python, R, SQL

  • Libraries: Scikit-learn, TensorFlow, PyTorch, NumPy

  • Visualization: Tableau, Power BI, Seaborn, Plotly

  • Cloud: AWS, GCP, Azure

  • Version Control & Workflow: Git, Jupyter, VS Code, Airflow

5. WORK ENVIRONMENT

  • Locations: Tech companies, consulting firms, banks, hospitals, retail HQs, government

  • Industries: Finance, Healthcare, Marketing, E-commerce, Cybersecurity, Manufacturing

  • Schedules: Full-time, hybrid/remote common

  • Environment: Team-oriented; heavy laptop use; meetings with product, engineering, and business leads

6. JOB OUTLOOK

  • Demand Drivers: Big data, AI/ML adoption, automation, data-driven decision-making

  • Annual Openings: ~20,800 projected through 2033

  • Growth Opportunities: Transition to machine learning engineer, product analyst, data science manager

7. RELATED OCCUPATIONS

  • Role: Actuaries | Salary: $125,770 | Education Level: Bachelor’s degree

  • Role: Software Developers | Salary: $131,450 | Education Level: Bachelor’s degree

  • Role: Market Research Analysts | Salary: $76,950 | Education Level: Bachelor’s degree

  • Role: Economists | Salary: $115,440 | Education Level: Master’s degree

  • Role: Operations Research Analysts | Salary: $91,290 | Education Level: Bachelor’s degree

8. RESOURCES FOR LEARNING & ADVANCEMENT

Courses & Certifications:

  • Coursera: Google Data Analytics

  • edX: HarvardX Data Science Professional

  • Springboard: Data Science Career Track

  • DataCamp, Khan Academy (Statistics, Python), MITx

Books:

  • Data Science from Scratch by Joel Grus

  • Python for Data Analysis by Wes McKinney

  • Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow by Aurélien Géron

  • Storytelling with Data by Cole Nussbaumer Knaflic

Communities:

  • Reddit: r/datascience, r/MachineLearning

  • Kaggle (competitions & datasets): https://www.kaggle.com

  • LinkedIn Groups: “Data Science Central,” “Women in Data”

  • Discord: “DataTalks.Club,” “Data Science Career”

Videos & Podcasts:

  • YouTube: “StatQuest with Josh Starmer,” “Ken Jee,” “Krish Naik”

  • Podcasts: Data Skeptic, Not So Standard Deviations, Lex Fridman

9. REGIONAL DATA & EMPLOYMENT TRENDS

Keandra Wilson
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