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Lead scientist vs data scientist

The differences between lead scientists and data scientists can be seen in a few details. Each job has different responsibilities and duties. It typically takes 2-4 years to become both a lead scientist and a data scientist. Additionally, a lead scientist has an average salary of $110,028, which is higher than the $106,104 average annual salary of a data scientist.

The top three skills for a lead scientist include C++, java and data analysis. The most important skills for a data scientist are python, data science, and visualization.

Lead scientist vs data scientist overview

Lead ScientistData Scientist
Yearly salary$110,028$106,104
Hourly rate$52.90$51.01
Growth rate17%16%
Number of jobs49,455106,973
Job satisfaction--
Most common degreeBachelor's Degree, 52%Bachelor's Degree, 51%
Average age4141
Years of experience44

What does a lead scientist do?

A lead scientist is primarily in charge of leading the efforts in conducting scientific studies within a particular program or project. Their responsibilities revolve around setting goals and objectives, delegating tasks, establishing guidelines, and overseeing the progress and performance of other scientists and workers in a laboratory. They may also liaise with clients and external parties, including the media. Furthermore, as a lead scientist, it is essential to encourage the team to reach goals, all while implementing the laboratory's safety policies and regulations to maintain a safe and productive work environment.

What does a data scientist do?

A Data Scientist analyzes information from multiple sources in order to gain maximum insight that can give the company a competitive advantage. They work in different domains, including manufacturing, healthcare, education, and finance.

Lead scientist vs data scientist salary

Lead scientists and data scientists have different pay scales, as shown below.

Lead ScientistData Scientist
Average salary$110,028$106,104
Salary rangeBetween $78,000 And $153,000Between $75,000 And $148,000
Highest paying CitySanta Cruz, CARichmond, CA
Highest paying stateTennesseeCalifornia
Best paying companyPayPalThe Citadel
Best paying industryTechnologyStart-up

Differences between lead scientist and data scientist education

There are a few differences between a lead scientist and a data scientist in terms of educational background:

Lead ScientistData Scientist
Most common degreeBachelor's Degree, 52%Bachelor's Degree, 51%
Most common majorChemistryComputer Science
Most common collegeUniversity of Southern CaliforniaColumbia University in the City of New York

Lead scientist vs data scientist demographics

Here are the differences between lead scientists' and data scientists' demographics:

Lead ScientistData Scientist
Average age4141
Gender ratioMale, 70.9% Female, 29.1%Male, 79.6% Female, 20.4%
Race ratioBlack or African American, 6.2% Unknown, 4.1% Hispanic or Latino, 9.4% Asian, 26.5% White, 53.7% American Indian and Alaska Native, 0.1%Black or African American, 4.2% Unknown, 5.4% Hispanic or Latino, 6.9% Asian, 18.8% White, 64.2% American Indian and Alaska Native, 0.6%
LGBT Percentage8%9%

Differences between lead scientist and data scientist duties and responsibilities

Lead scientist example responsibilities.

  • Manage cell culture facility transition to GLP compliance.
  • Develop reagents for ELISA methods and manage regulatory aspects of vaccine potency assays with the USDA/EMEA.
  • Manage pediatric dose development project, technology transfer project and alternate API supplier qualification project on budget and on schedule
  • Engage in ELISA base in vitro vaccine potency assay development.
  • Develop new HPLC methodologies for new and existing products as per USP.
  • Serve a key role in the compliance of quality and FDA regulations.
  • Show more

Data scientist example responsibilities.

  • Update, maintain, and manage regional CRM database and records for customers, vendors, and suppliers.
  • Configure and manage JobScope ERP system for a make-to-order/make-to-stock design and manufacturing environment.
  • Lead the analysis in SAS for data integration of mortality data using meta-analysis integration methods.
  • Implement a proximal stochastic gradient descent with a line search to fit a regularize logistic regression in Scala
  • Perform cross-validation-test on linear regression model of data using scikit-learn.
  • Develop python base statistical visualization to provide insights of fuzzy social media data.
  • Show more

Lead scientist vs data scientist skills

Common lead scientist skills
  • C++, 7%
  • Java, 6%
  • Data Analysis, 5%
  • GMP, 5%
  • Method Development, 4%
  • Extraction, 4%
Common data scientist skills
  • Python, 13%
  • Data Science, 10%
  • Visualization, 5%
  • Java, 4%
  • Hadoop, 4%
  • Tableau, 3%

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