New Report - State of Mathematical Optimization in Data Science

Prévia do material em texto

State of Mathematical 
Optimization in Data Science
2024 Report
Introduction
Today’s data scientists play a critical role in shaping 
data-driven strategies that guide organizations to 
success. But as their field continues to evolve, data 
science professionals must ensure they have the 
right tools and approaches to tackle increasingly 
complex problems. 
To better understand how this fast-growing job field 
is changing, we surveyed over 1,086 data scientists 
about their workflows, challenges, and needs. 
Now in its fourth edition, the State of Mathematical 
Optimization in Data Science survey aims to 
provide a comprehensive, year-over-year analysis of 
emerging trends affecting data scientists.
In this report, we’ll delve into current findings to 
explore how data scientists are navigating the 
latest industry shifts and embracing advanced 
methodologies, including mathematical 
optimization.
We’ll also examine the latest trends in data science 
skill development to offer practical guidance on how 
data professionals and their organizations can foster 
growth and innovation.
75% of respondents expressed a desire to strengthen their 
analytics skillset by learning more about mathematical 
optimization.
Data scientist insight
Methodology
In 2024, we surveyed 1,086 self-identified 
data scientists via an online survey platform 
about their team needs, workflows, ongoing 
skills development, and use of technology. 
Respondents were sourced from KD Nuggets 
and Data Science Central subscriber lists. 
All respondents are current practitioners, 
independent consultants working on data 
science projects, or data science/analytics 
leaders. A plurality (42%) of respondents 
have worked in a quantitative/technical role 
for 11 years or more, 14% have done so for 
7-10 years, 14% for 4-6 years, and 30% for 3 
years or less.
2 | 2024 Report State of Optimization in Data Science
of respondents said they are 
familiar with mathematical 
optimization, and 70% of 
those respondents defined 
it correctly.
No. 1 65%
Key findings
of those using mathematical 
optimization currently use it 
in combination with machine 
learning.
No. 4 59%
of respondents use heuristics 
because it’s “good enough.”
No. 2 47%
No. 3
of data scientists consider 
themselves to be self-taught.65%
3 | 2024 Report State of Optimization in Data Science
Business Needs 
Drive Learning & 
Development Plans 
Section 01
While all data scientists need a certain baseline knowledge of mathematics, statistics, and computer science, 
no single degree, certification, or path is required to become one. Some have received formal education in the 
field, while others have learned on the job. In fact, some companies appear to be investing in long-term talent 
development for data scientists.
Regardless of how they got there, once a data scientist lands a job in their field, business needs tend to direct 
their annual training goals. In addition, communication and other soft skills are in high demand for new hires.
4 | 2024 Report State of Optimization in Data Science