Machine Learning Specialist Resumes & Guide

Last Updated on November 14, 2022

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It can’t be denied that the demand for Machine Learning Specialists is rising daily. If you want to score big on your dream gig, your resume needs to be on point and rocking! Our sample will help you create a resume that will leave jaws dropped, and eyes popped. 

We will explain how you can make a resume into an interview-winning document with our Resume Guidelines for Machine Learning Specialist Roles.

Machine Learning Specialist Resumes

(Free sample downloads are at the bottom of this page)

Machine Learning Resume Writing Guide

Resume Sections

1. Contact information
2. Profile Summary
3. Work History 
4. Achievements
5. Education 
6. Skill Section
7. Certification & licensing
8. Extras: Languages/Awards/Publications/Volunteering/hobbies

What to Highlight in ML Specialist Resume

Ok, so luckily, you don’t have to worry about impressing other machine learning specialists (Phew!). But the people you DO have to impress are the top gun, corporate vision types. What gets them buzzing like an electric wire? Appearances and professionalism. 

Your resume format needs to be in top form. 

  • Whether you’re highly experienced or a newbie starting out, recruiters will still be looking for the same set of generic skills you all should have. These skills determine if your resume gets put in the YES or NO pile. 
  • It’s super tempting to send a whole bunch of copied and pasted resumes, but please don’t do this. It’s so important to personalize your resume to each potential employer’s stipulations. 
  • Is the job ad asking that an applicant have certain programming knowledge, like proficiency in Python? That skill should be brought to the forefront of the resume. 
  • Finished a certification/ course that is related specifically to a condition listed in the job ad? Showcase it in your resume.  
  • Supervised a specifically related project relating to experience the recruiter asked for, like a Natural Language Processing (NLP) project? Please include it in your resume.
  • Include expertise in scripting languages. Expressly, the language most used by employers was Python, but Java and Scala were frequently mentioned. (If you want to improve your Python skills, you can do it through Springboard’s free course.) Don’t forget to explain them in your skills section.
  • You must prove a deep understanding of everyday machine learning concepts. If you‘ve completed any previous academic research in machine learning, add this as it’s a big plus for your resume.
  • Basic knowledge is vital to a very machine learning job. Recruiters will be looking for terms like statistics, algorithms, data processing, and prior use of engines like GPU computing, Apache Spark, data mining, and Agile software in the resume. 
  • Lastly, highlight your industry experience. Because Machine Learning Specialists are found in almost every work environment now, you will have to be specific here and explain everything very concisely. 

Career Summary & Objective

Every resume has a few sections that can remain generic, like Education, Tools & Tech, and Soft Skills. A Machine Learning Specialist’s resume is no different. 

The Career Summary/Career Objective and Job Duties sections must have room for personalization. You should rewrite them in new ways for each application you send. It sounds like more work, and it is but not that much, plus it will increase your chances of securing an interview. 

A resume summary or resume objective will catch the eye of the recruiter. 

Keep in mind that a resume summary is for the Machine Learning Specialist with loads of experience, and you use that experience to prove you’re right for the job.  

A resume objective is a clever plan for the resumes that don’t have heaps of experience as it sells your skills and passion. 


Make sure it has an adjective, years of experience, your goal, and your resume’s most “oh my lord, wow” achievements. Have you ever worked as an SQL developer? Highlight the top moments from that job or from high school.

Resume Hack: Repeat the keywords found in the job ad in your career summary. For example, if the job entails advanced coding skills and applies to you, use the term “coding,” not “programming.” Obviously, they mean the same thing, but the ATS Bot isn’t aware of that, and is preset to look for the word “coding” during the automatic screening process. This is called Resume SEO, and it will drastically improve your chances of your resume being shortlisted and reviewed by the actual human recruiter!

Three Examples

Summary Example 1

“Avid machine learning engineer with 5+ years’ experience in predictive modeling and data mining. Enthusiastic to apply statistical machine learning solutions for Macro Globe. At Stack Intellect, instigated demand forecasting models increasing forecast accuracy by 39%.”

Summary Example 2

“Driven machine learning engineer with proficiency in data mining and algorithms. Looking to enhance machine learning models for Network Inc. As junior SQL developer at Aegir Vital, created and enhanced 55+ stored procedures and functions that decreased data retrieval time by 23%.”

Summary Example 3

“Entry-level machine learning engineer with deep knowledge in data mining and machine learning. A proficient individual with exceptional communication and presentation skills. Can work self-sufficiently and easily adjusted in a team. Proficient in outstanding designing and maintaining of machine learning models.”

Employment History

You, my friend, are a Machine Learning Specialist. The C-suite has no cooking clue what you do, but they must know you’ve done it. If you don’t have a Ph.D., your experience is make-or-break in your resume. This is how we suggest you list your history of employment:

  • Give your current/ latest job title.
  • The name of the company and years/months you worked there.
  • Put in a concise machine learning engineer job description.
  • Include 4–5 bullet points (less if you have many older jobs).
  • Tell the story with the Problem-Action-Result (PAR) format in every bullet point.


Senior Machine Learning Engineer at Ford

February 2017 – December 2023

Working with product managers, formulating the data analytics problem with the purpose of developing, simulating, testing, and improving algorithms for the prediction of electrical load and generation.

  • Devised and established analysis systems to obtain information from large- scale data.
  • Created a customer segmentation algorithm in R resulting in a 28% increase in market shares.
  • Enhanced personalization algorithms for applications with 3M+ users.
  • Utilized data mining to shipping consolidation problem, saving $1.8M.
  • Projected product sales to within 1.3% by using logistic regression model.
Machine Learning Engineer at Coke

February 2016 – January 2019

Responsible for using machine learning and statistical modeling methods to develop and evaluate algorithms used for improving quality, performance, data management, and accuracy.

  • Conducted quantitative research that focused on utilizing advanced statistical learning methods on varied data sets to create robust models for predicting stock risk and returns.
  • Modeled the performance of important investors. 
  • Built effective tools and scalable systems for the team. 
  • Generating ideas, backtesting, and implementing ideas.
  • Evaluated new datasets for possible addition to the investment models. 

Job Descriptions, Responsibilities, and duties

Today, Machine Learning Specialists are in almost every industry you can think of with substantial deviation in their role purposes and KPI’s. In saying this, a recruiter expects to see certain proven general responsibilities and proficiencies within a candidate's resume. This depends on the educational level and the stage of the career of the candidate. 

Job Duties Samples for Machine Learning Specialists

Entry-career stage (0-2 years’ experience):
  • Using machine learning and statistical modeling methods to develop and evaluate algorithms used for improving quality, performance, data management, and accuracy.
  • Working with Adobe’s research teams, taking state-of-the-art research, and making amazing products and features.
  • Working with product managers, formulating the data analytics problem.
  • Developing, simulating, testing, and improving algorithms for the prediction of electrical load and generation.
  • Designing lean proofs of concepts (POC) aimed at answering targeted business questions. 
  • Exploring and working with a broad range of proprietary and interesting data stores and applying existing methods or developing new methods. Be creative!
  • Build new models to extract more value from the customer data being collected, which helps us know more about users and merchants than any other company in marketing or retail.
Mid-career stage (2-4 years’ experience):
  • Providing influence and technical advice to junior- level team members as they develop solutions. 
  • Developing and adapting the best practices for designing and building medium complexity machine learning systems. 
  • Staying informed with the latest machine learning trends and methods
  • Proficient in at least one of the programming languages like Matlab, Python, Java, R, C++. 
  • Contributing to designing and prototyping of medium- to- high complexity machine learning systems.
  • Having a passion for all things machine learning- related.
  • Proficient with at least one of the following applicable machine learning packages: Scikit-learn, R, Weka. 
Experienced/advanced stage (4-6 years’ experience):
  • Cooperating with others in creating and executing your technical vision.
  • Learning more about what is being done at and 
  • Providing technical leadership to a solid team of data scientists, analysts, and engineers. 
  • Building incredibly parallelized, big data, machine-learning applications in the cloud. (Sounds like a Mad Lib of marketing jargon, but seriously…that’s what you build). 
  • Demonstrating technical project or product development leadership. 
  • Expert in using data analysis languages like Scala, Python, or R. 

Highlight Your Accomplishments 

All too often, our resumes end up becoming this epic list of all the duties we’ve ever had in our previous jobs. DO NOT DO THIS! It is super boring to read, lowers your accomplishments, and it never really highlights just how good you are at your job.

Instead, what you want to do is showcase all the most impressive achievements and projects you helped out on/ managed. Your daily duties are of some interest to the recruiters, don’t get us wrong, but they are more interested in how much you can do for their business. That’s why it’s so important to shine a great light on all your accomplishments, making it easy to see the value you could add to the company if they hired you. 

You need to prove your accomplishments now

We have one word for you: QUANTIFY! Highlight the results of your best achievements by using quantifiable values like numbers, percentages, etc. Be careful not to make the accomplishment statements out-of-this-world boring to read, however!

Examples of boring statements (not what we want):

  • Created molecular dynamics simulations utilizing machine learning algorithms, identifying protein-DNA exchanges.
  • Decoded intricate simulation datasets utilizing statistical methods.
  • Enhanced accuracy of simulation utilizing complicated algorithms.
  • Modernized and enhanced stored procedures utilizing T-SQL.
  • Created PL/SQL stored procedure, functions, and style sheets to decrease data retrieval time.  
  • Re-designed schemas with tables to improve the integrity of data.

Quantified and made sound: 

  • Created molecular dynamics simulations utilizing machine learning algorithms, identifying protein-DNA exchanges with up to 92% dependability.
  • Decoded 270+ intricate simulation datasets utilizing statistical methods.
  • Enhanced accuracy of simulation up to 54% utilizing complicated algorithms.
  • Modernized and enhanced 49+ stored procedures utilizing T-SQL.
  • Created PL/SQL stored procedure, functions, and style sheets to decrease data retrieval time by 64%.  
  • Re-designed schemas with 230+ tables to improve the integrity of data.

Education Section & Examples

This section is way more critical than you think. It is a crucial part of your Machine Learning Specialist resume. A master’s degree in mathematics, computer science, or a similar applicable field is a must in this field. 

Recruiters must have formal proof that you’ve mastered: 

  • data structures (queues, stacks, trees, multi-dimensional arrays, graphs) 
  • algorithms (sorting, searching, dynamic programming, optimization)
  • computability and complexity (NP-complete problems, P vs. NP, big-O notation, approximate algorithms) 
  • computer architecture (cache, memory, deadlocks, bandwidth, distributed processing)

Remember to include formal (or self-taught) courses for coding languages, like Python, JavaScript, C++, Julia, C#, Java, Shell, TypeScript, R. In this field, these languages are the most commonly used.

Ok, so how do we do this? Simply put: Give them the date you completed your degree, the name of your degree, where you studied, and the city and state abbreviation. Include any training programs, online courses, or industry workshops and give the details about the course curriculums of program topics.


2009-2013- Ph.D. in Machine Learning. Carnegie Mellon, Los Angeles, CA

September 2018- Research paper: Machine Learning, Probabilities Explained, published in Journal of Cryptology. Senior data mining project featured in TechCrunch. Surpassed expectations in data structure and database coursework.

2016–2014- Master of Science in Machine Learning. Carnegie Mellon, Los Angeles, CA

2011–2013- Bachelor of Science in Computer Science. University of Washington, WA. Major Subjects:

  • Data and Quantitative Analysis
  • Decision Analytics
  • Predictive Modeling

The Resume Skills Section

Unfortunately, you cannot generalize this section. It’s all dependent on what skill set the company is looking for. We know you are bursting the seams with all your skills, but you simply can’t list them all. You must categorize them and find your best ones that fit with the job ad. After that, prove each of them as shown below.

Technical skills aren’t the only skills that matter here. Machine Learning Specialists are often found working in business settings where they need to communicate intricate concepts and make decisions based on data. Because of this, your communication, leadership, and analytical thinking skills need to be off the chart. These are called Soft Skills

A Skills Matrix is a great way to highlight all your relevant skills at once professionally and concisely that also just looks good! 

Professional Skills Matrix
Technical CompetenciesInterpersonal TraitsTech Skills
Data modeling and evaluationInterpersonal SkillsC
System designPresentation SkillsC++
Programming languagesTeamwork and CollaborationJava
Software engineeringWritten and Verbal CommunicationPython
Data analysis and interpretationProblem SolvingR-Shiny
ML libraries & algorithmsAttention to DetailSQL
Signal processing techniquesTime ManagementR Shiny

Qualifications & Certifications associated with Machine Learning Specialists

Data Science and Machine Learning Bootcamp with RIntroduction to Machine Learning for Data ScienceA-Z Machine Learning using Azure Machine Learning (AzureML)
The Complete Machine Learning Course with PythonJavascriptHadoop
PandasPh.D. in various AI, Computer & Big Data DisciplinesProgramming in C#
Programming LanguagesMachine Learning AlgorithmsGraphics Processing Unit

Optional Extras

Distinguishing your resume by lining it up with the job advertisement makes it easier for the recruiter to picture you working for their company. 

The personalized resume allows recruiters to see exactly how well you’d be able to do the job. 

Put in a section with all your volunteering in your resume highlighting your philanthropic side to show the recruiters you can fit into their social mission. 

In your “others” section, highlight some of your passions that show your depth as a person. 

Any Machine Learning Projects you’ve done. 

  • Made social media sentiment analyzer that keeps track of 145 million posts daily.
  • Established data mining algorithms for 16 online clients.

See Portfolio at

Machine Learning Papers. 

  • Machine Learning Practical Futures published in The Computer Journal, August 2013. 
  • AI, Big Data, and the Internet of Things, published in TechWallop, February 2017. 

Member of the Association for Computing Machinery. 

  • Allied 70+ new machine learning engineers with experts in their fields.
  • Regularly networked with 1300+ members online as a social media director.

Professional information 

Sectors: IT, Telecommunications, Defense and Military, Government, Manufacturing, Automotive, Aviation, Transport, Banking,
Career Type: Artificial Intelligence, Data Sciences, Engineering, Mathematics, NLP, Machine Learning, Robotics
Person type:  Tracker, Detector, Coder, Programmer, Modeller, Engineer, Scientist
Education levels: Bachelor’s to Masters’ Degree
Salary indication: An average of $114 121 (Glassdoor)
Labor market: Average of 16% growth between 2018 -2028 (
Organizations: SME, Fortune 500, Government, NPO, Corporate, Commercial,  

Machine Learning Resume Downloads