Andrew Smith

Professional Summary

Diligent Analyst with 3 years of professional experience.Seeking an opportunity to leverage my diverse skills and experience in predictive modelling and data visualization.Skilled in machine learning,analytical problem solving and programming .Looking for more challenging roles to learn and excel further in this field.

Employment history

DATA ANALYST, Lakin LLC. Moenland, Delaware
Jan. 2020 – Present
  • Designed and developed a predictive analytics solution for identifying the recharge patterns of the customers for one of the largest telecom companies  of South America
  • Utilized last 3 months data for time series clustering analysis to identify the latest characteristics of customers against their recharge behaviour 
  • Recommended the best customized plan of recharge based on the insights inferred from the above analysis.
  • Identified the potential customers  for churn and predicted the churn propensity of the customers.
TECHNICAL CONSULTANT, Beier, McCullough and Rosenbaum. Cormierport, Vermont
Aug. 2016 – Dec. 2016
  • Data mining and data visualization as Proof-Of-Concepts on data ingested from multiple sources.
  • Design and development of technical architecture for web-based GUI leveraging R.
  • Maintaining of online training portal developed for predictive analytics methods.


Murray University, Lake Leliabury, Virginia
Master of Science, Statistics, 2016
The Kihn, Alyciaside, Rhode Island
Bachelor of Science, Statistics, 2014

Personal Skills


Personal info

Phone: (000) 000-0000
Address: 287 Custer Street, Hopewell, PA 00000





Andrew Smith

287 Custer Street, Hopewell, PA 00000
(000) 000-0000

Professional Summary

Recent graduate of Software Engineering with a BSBA major in Operations Management with specialization in Business Analytics. Passionate on learning and understanding the business to help further contribute to the company needs. Has a grit on developing new skills on programming. By combining these to my knowledge in Business, I'll be able to provide the best outcome for the clients.

Employment history

Benefits Administrator Specialist, Kuhic Inc. Feeneyburgh, Hawaii
Oct. 2018 – Present
  • Process documentation, maintaining data and generating reports for leave of absence premium administration
  • Maintain 100% Process Accuracy.
  • Process Flow Documentation for different processes
  • Extracting information from different reports
  • Coordinating process steps to Client overseas
Junior Data Analyst, Powlowski, Kerluke and Schimmel. Delborough, California
Feb. 2018 – May. 2018
  • Search for, collect and verify data for companies under his/her assigned market based on current collection methodologies and acceptable sources
  • Attend to internal/client queries and requests to ensure data captured is aligned with data methodology and policy guidelines
  • Understand documentation from varying business industry

Note: this is a seasonal employment

Technical Support Representative, Schroeder Inc. West Arlean, Oregon
Sep. 2017 – Oct. 2017
  • Clearly identify and understand the different problems of the customers, and provide the right solutions every time
  • Communicate with different nationalities and deliver the solution step by step
  • Explain different technical terms to the customer in layman's term


Western Moen University, Lake Eliana, Montana
Certificate of Completion, Software Engineering, May. 2019
Southern Hand, New Fosterburgh, Maryland
Bachelor of Science, Business Administration major in Operation Management with Specialize Track on Business Analytics, May. 2017


Node JS
Express JS
Mongo DB
BootStrap 4
Process Flow Designing and Documentation

Andrew Smith

Professional Summary

My Interests and experience in Data Science lies in Exploratory Analysis,building ML Models/Algorithms, statistics and data Visualization. I package my skills alongside strong ability to clearly explain technical and analytical information (verbal, written and in presentation form) and summarize for key stakeholders.

Employment history

Data Analyst, Bartell-Hoppe. Lake Seanfurt, Montana
Aug. 2017 – Present
Projects in Synchrony:

Interactive Voice Response (IVR) is a telephony menu system that enables identification, segmentation and routing of callers to the most appropriate agent within your team. It is a simple and effective and will significantly reduce costs and increase efficiency within any company.
It is an electronic device that is connected to a telephone line to monitor the dialed numbers and alter them to seamlessly provide services that otherwise require lengthy National or International access codes to be dialed. A dialer automatically inserts and modifies the numbers depending on the time of day, country or area code dialed, allowing the user to subscribe to the service providers who offer the best rates. 

The Fraud Index (2.0) model:
Objective - To identify true name fraud (TNF) at the time of acquisition for Private Label
Credit Card (PLCC) and Dual Card (DC) credit-approved population.
Approach - Given the size of the data, sampling was performed by ensuring good
representation of all products. Unbalanced data was treated using SMOTE. Stratified
random sampling was used to split data. Used credit bureau variables and decision tree
technique to come up with low loss rate segments (< 5.5%) for the business.
SVM, RF, Lasso regression, AdaBoost and XGBoost were used to
build the models. XGBoost was selected based on KS, Gini and AUROC as accuracy

Malicious Intent Proof of Concept:
Objective - To identify and provide likelihood of malicious intent on the portfolio.
Approach - Utilized data lake & R programming for sampling, data cleaning, feature
engineering and model building. Gradient boosting, random forest and logistic regression
were tried while building the model. We went ahead with RF delivering quick win

SAGC credit risk model:
Objective - To build credit scoring rule that can be used to determine if a new applicant is a
good credit risk or a bad.
Approach - Logistic, RF, SVM and lasso regression were used and a thorough model
comparison was done based on various parameters such as AUC, KS Goodness of fit test and
Gini. Prior to model dev, Univariate and Bivariate analysis was done for variable selection
based on IV and WOE values, Multivariate analysis was done for dimension reduction using
variable clustering approach.

Explored business opportunities to increase credit card sales of our partner:
Identified highly engaged cardholders, medium engaged & other category shoppers using kmeans
clustering technique & analyze investment opportunities using a spend sensitivity
Prior to Clustering, data cleaning, outlier treatment like capping variables and missing
value imputations were done.


● Awarded Shining star (Innovation Driven) to automate the process of removing duplicate accounts from Atomix DB.
● Awarded Shining Star (Strong Execution) after developing weblink using PHP and HTML to provide a solution to the customer service agents to exclude phone number from Dialing till customer request promise to PAY.
● Appraisal rating for 2017-2018 is Outstanding Star for my year performance.
Senior Software Engineer, Murphy, Larkin and Wilkinson. Gerholdport, Iowa
Sep. 2015 – Oct. 2015
Project:- BT Javelin:
This project deals with the end to end data flow using concept of data warehousing. The flow of data across the different layers of data warehousing:
▪ DRA (Data Reception Area)
▪ HLD (Holding Area)
▪ CDM (Common Data Model)
▪ DM (Data Mart)
▪ DM to OBI

Responsible for running ETL scripts and verifying the data after loading. E2E responsible to move the ETL code changes from test to Production. Writing Shell scripts and DB query's as per requirements. Also, responsible to handle production issues and provide root cause.
Technical Associate, Wisozk, Waelchi and Swift. Lake Josettefurt, Connecticut
Oct. 2012 – May. 2013
Project : ADLI-Phoenix

Phoenix is a logical inventory for British Telecom’s Openreach assets and will manage fiber logical network and service inventory for FTTC, FTTP and Ethernet network. Phoenix will host various capability interfaces to support lead to cash, Plan & build and trouble to resolve areas of FTTC. It has interfaces with different client systems likes Flow, Hub, Smarts, RRT, NEO, NH21, IRAMS etc from where it receives on line request from these systems

Responsible for solving the prod issue base on the ITIL SLA and automating the process using shell scripts to monitor pro-actively and to reduce manual efforts. Also, writing DB query's to extract the data on client request.


Beier Institute, Lake Dallas, Nevada
B.TECH, CSE, Feb. 2009

Personal info

Phone: (000) 000-0000
Address: 287 Custer Street, Hopewell, PA 00000


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Long gone are the days when all a data analyst had to do was operate a few computer programs. The data analysts of today’s working world are versatile and dynamic workers, highly regarded by their companies for the sheer amount of value they bring to the table. 

If you’re interested in applying to a data analyst position, you’ll need the perfect resume. That can seem a little daunting, particularly for such a technical position, so we’ve decided to create the definitive guide to crafting a world-class data analyst’s resume.

We’ll be covering the hard & soft skills you’ll need (as well as how to reference them), the best formatting tips, how to write a perfect resume objective or summary, and even how to write a data analyst resume if you have no experience. Towards the end of the guide you’ll find advice on standing out from the crowd as a data analyst, in addition to a few tips on what goes into making a truly effective data analyst’s resume.

Template Examples

How to Format the Resume

The best format for a data analyst’s resume is the reverse-chronological style. What makes this format so impressive is that it highlights your skills and experience by putting them right at the top of the document. Since you can’t count on a recruiter spending more than a few seconds on each resume, it’s vital that you grab their attention sooner rather than later.

Stick to classic fonts, such as Times New Roman, and don’t overcomplicate the headings you use. Make sure you use plenty of white space, as a way to make life easy on whoever’s reading your resume. When it comes to contact information, keep it simple: your name, current phone number, and professional email address are all you need to consider.

What Recruiters Will Look For

It’s important to be able to look at the hiring process from the recruiter’s POV. Over and above the formatting and any relevant experience you may have, the person going through candidates for the position will be on the lookout for some particular traits, which they have identified as crucial to the success of the new employee. These tend to vary from company to company, as every enterprise has its own personal and business outlook, but there are some common factors that will apply to every data analyst’s resume owing to the nature of the job. Recruiters sorting through a pile of data analyst resumes will be looking for a few key traits:

1. Analytical Ability 

Your employer needs to know you can handle any technical challenges you'll encounter. This is the most important aspect of a candidate’s proposal; if your analytical skills aren’t up to par, a business might baulk at the idea of giving you such a crucial, technical role as a data analyst.

If you can succeed in making your analytical ability clear and obvious, you’ll be making the rest of what you offer even more impressive. If your technical skills are beyond all doubt, the recruiter will already be positively predisposed to the rest of what you offer as a candidate.

2. A Problem-Solving Attitude

You’ll need to show the recruiter that you’ve got the right attitude when it comes to solving problems. Being open-minded, creative, and proactive goes a long way. You’ll have to solve problems day in, day out. 

The best way to show the recruiter you’ll be able to handle such issues is by making it clear you’ve got the right attitude. With the right problem-solving mindset, you’ll be able to promise consistency, even though the problems you come up against will be varied and diverse.

3. Interpersonal Competence 

An analytical wizard with no communication ability isn’t all that useful to a business. You need to prove that you can work in a team and present your results to non-experts. This is especially important in today’s business environment, which is dynamic and multi-faceted.

As a data analyst, you can expect to spend some time working with individuals and groups from other departments. Being able to explain the work you’re doing in simple, jargon-free language will go a long way towards making you an attractive candidate.

Which Skills to Mention and How to Do It Correctly

For your resume to stand out, you'll need to highlight more than just your technical abilities. The key concept to get your head around is the division between hard and soft skills. Including each group of skills in the right way will go a long way.

Hard Skills

For a data analyst, hard skills include:

  • Being able to use Tableau or similar software packages
  • Being able to code in languages like SQL, R, and SAS
  • Familiarity with Data Warehousing
  • Being so familiar with the field of statistics that you can adapt to whatever the job at hand requires 

When you’re writing about your hard skills, it’s important to keep things short and to the point. You might want to wax lyrical about how well you know your trade, but writing too much could actually do more harm than good, and here's why:

Your potential employer wants a capable analyst who can handle their work efficiently. Being direct in your writing about the hard skills you have is a great way to communicate how precise you can be. A good rule of thumb to keep in mind is that less is more when it comes to the technical side of this position.

More specific hard skills that would be relevant to your work as a data analyst include:

Statistical Modelling
  • Regression (linear and logistic)
  • Forecasting 
  • Principal components analysis & factor analysis
  • The basics, such as sampling, bias, model validation, etc.
Machine Learning 

Experience with machine learning isn't expected for every data analyst job. If you can include in your resume, though, you'll really impress the recruiter.

  • Artificial neural nets
  • Decision trees
  • Experience using machine learning programs 

It's important that you can present your findings in a visual, engaging way to non-experts in the field. To this extent, PowerPoint skills will come in handy, as will being proficient with Excel.

Soft Skills

When you’re talking about your soft skills you’ll need to communicate in a way that inspires a connection. This stands in contrast to the crisp, concise way you should discuss your hard skills.

You need to make sure the recruiter gets a sense of you as a well-rounded, open individual. Leadership, interpersonal effectiveness, and having the mentality of a team player all go a long way. Here are some of the soft skills to consider including:

  • Communication
  • Interpersonal effectiveness
  • Leadership
  • Delegation
  • Being a team player
  • Time management 
  • Cutting down on jargon

The Best Way to Include Digital Skills in a Data Analyst’s Resume

Data analysts need to draw on many different skills during the course of their day-to-day work, and most of them are technical and involve the use of various pieces of software. How to reference these digital skills on a regular resume isn’t always obvious, but this is how you should go about it.

The best way to include digital skills on a data analyst’s resume isn’t to list them all together, but rather to group them into two main headings — analytical skills and more standard technical skills. 

Under the first category you should include proficiency with statistics, as well as with any relevant software packages (such as Tableau or Excel). Each skill you list will also have a more impressive effect on your prospective employer if you tie it in to what you’ll be able to use it for on the job. For example, rather than simply mentioning that you’re used to using q, it’s a good idea to throw in the fact that you can use your Q experience to analyse customer behaviour.

As for the standard technical skills, you’ll want to talk about the different computer languages you’re comfortable coding in. Programming is becoming a more and more important quality for data analysts to have. The same rule applies here, too; rather than claiming you’re experienced in SQL, make sure you mention what kind of common problems your SQL knowledge can help you to solve, e.g. designing a custom query to prompt the database in question to return only very specific fields. 

How to Write a Resume Objective & Examples

A resume objective should make it clear to the employer that you have the requisite skills, experience, and personality traits to fit the specific position you’re applying for. Generalities should be avoided at all costs — this is your chance to show you thoroughly understand what they’re looking for. 

The first step of writing a resume objective is to figure out what traits, skills, and experience the ideal candidate would have. This can be done by carefully reading the listing and comparing it to other data analyst job listings to see what’s different. Once you know what they’re looking for, craft your objective statement in a way that makes it clear you’re the right candidate for the job. Be holistic: mention experience and your skills, but also talk about the personality traits you possess that would make you a good choice. 

Here are a couple examples of well-written resume objectives:

  1. Organized, passionate data science professional seeking the role of Data Analyst at Example Inc. I have worked extensively with SQL, R, SAS, and data warehousing. I’m used to modelling data sets to improve business efficiency, and have a lot of experience functioning as the leader of a team in order to solve particularly complex problems.
  1. Knowledgeable Data Analyst interested in filling that role within ABC Corp. Bringing 8 years of experience with designing database procedures and processes to the table. Keen on bringing programming acumen (R & SQL), creative thinking, and a no-nonsense approach to jargon to the table. Fascinated by data warehousing, always willing to learn new things about the field I love.

How to Write a Resume Summary & Examples

The best way to think of a resume summary is as an elegant way to highlight your technical aptitude for the job. In the case of data analyst positions the summary is even more important, given the highly technical nature of the work. 

Your resume summary should go right at the top of your resume. The first paragraph could well be the only chance you get to command the full attention of the recruiter, so your goal is to make such a solid pitch that you encourage them to read further, and to pay attention to everything that comes below. Here are some examples of great resume summaries. Note their brevity and no-nonsense writing style, designed to make a sizable impact in the shortest possible amount of time.

  1. Driven, versatile data science expert interested in bringing 10+ years of experience to that position in XYZ Inc. Passionate about modelling business data to improve existing processes. Strong problem-solving & organizational skills, prior experience in successfully leading small teams. In my past work I’ve boosted operating efficiency by 7.5% as a result of creating in-depth models and identifying weak points in a strategic business model.
  1. Professional data analyst with 5 years of working experience interested in filling that role at Sample Corp. I’ve saved my past employer over $1,500,000 by identifying and resolving inefficient processes. I like brainstorming creative solutions to problems and then backing my conclusions up with hard data and figures. 

How to Write a Data Analyst’s Resume with No Experience

If you have no direct experience with data analysis, you’ll be at a disadvantage compared to other applicants. However, this disadvantage is by no means a fatal flaw to have in the job application process. As a matter of fact, if you structure your resume properly and nail the way you write it, you could well leapfrog over competitors with more experience but less impressive resumes.

The important thing to understand is that if you can sell yourself as somebody who is a quick learner, you’ll likely be able to offset the damage a lack of experience could cause. Large companies typically won’t have a huge problem with training a promising candidate in their discipline of choice, particularly if they believe the candidate in question would be a significant asset for other reasons. 

Here are a few guidelines to follow in order to make the most of a no-experience resume:

1. Talk about the Experience You Do Have 

Whether you lack experience because you’re transferring to data analysis from another field or whether it’s because you’re fresh out of college, chances are you do have some experience you can talk about in your resume. 

When you mention it, make sure to highlight the ways your skills are transferable to a data analyst position. This will kill two birds with one stone, as not only will you be proving that you’re somebody who can adapt to the demands of the job, but you’ll also be displaying an in-depth knowledge of what the job itself demands day in, day out. 

2. Don’t Try to Hide Your Lack of Data Analyst Experience 

Papering over the cracks will do more harm than good in this case. Rather than trying to hide the fact that you’ve never worked as a data analyst before, you should be upfront about it. Not only will the recruiters appreciate your honesty (after all, it’ll be obvious to them if you don’t have experience), you’ll also be presenting yourself as a confident, self-assured individual. Since you can’t sell your work history, you need to sell yourself.

How to Match Your Skills to the Position You’re Applying For

Even the most well-written resume will likely go unanswered if it’s clear that you haven’t tried to tailor your skills to the job post in question. The most efficient way to do this is to watch out for certain key words in the listing, which will serve as clues as to which skills you should emphasise and which should be relegated to the background. 

There’s no end to how many of these words there are, but it’s not too difficult to group them under overarching categories and work from there. The two main job requirements you’ll be prospecting for are as follows (with some examples of the words in question you’ll want to be on the lookout for):

  • Leadership. Data analysts are often required to take the lead on certain projects. If you come across words like manage, oversee, and direct, you’ll need to make sure you highlight your leadership skills. The best way to do this is by talking briefly about the project in question when you mention the skill, e.g. instead of simply mentioning that you have management experience, spending a sentence or two describing the project you took the lead on, and what you learned.
  • Communication. These days, practically no department works in isolation. If there are words like present, communicate, discuss, and integrate, you’ll need to highlight your softer skills, such as interpersonal relationship management, critical discussion, and delegation.
  • Analytic ability. Obviously you’re going to be doing some analysing at your prospective job, but the key takeaway here is to focus on using the same language used in the job listing to highlight your relevant skill set.

How to List Additional Details (e.g. Certifications, Online Courses, Volunteering, Hobbies)

Additional details can turn a good resume into a great one, even for a position as technical and granular as a data analyst. Not all additional details were created equal, though, with some playing a more important role than others. Here’s the hierarchy of extra information, ranked by importance to a data analyst’s resume:

1. Online Certifications 

There’s no end to the amount of online courses you can take these days, and recruiters know that as well. If you have any relevant certification you’ve completed online, mentioning it directly below your experience will prove that you’re an autodidact, capable of educating yourself but also motivated and dedicated enough to actually follow through on a project once you set your mind to it. Keep it short: by mentioning the course and the completion date in one sentence, you’ll be implying that the value of the qualification is self-evident, which will impress anybody reading your resume.

2. Awards 

Awards impress everybody, even if they’re not entirely related to the job at hand. If you’ve won anything significant, such as a prize in college or a high-placed finish in a Kaggle competition, mention it below your online certs and spend a sentence or two explaining what you won. Don’t be falsely modest here, but don’t be over-the-top either. Just provide enough information for the recruiter to get a sense of the magnitude of your achievement, and then leave it there. 

3. Volunteering

Volunteering demonstrates compassion, drive, and energy, particularly if you’ve kept it up for an extended period of time. When writing about any volunteering you’ve been up to, it’s worth spending a few brief sentences talking about how you chose that project in particular. 

4. Hobbies & Interests

The last bit of writing in your resume should be devoted to your hobbies and interests. One common mistake candidates applying to all kinds of jobs make is assuming their hobbies need to somehow relate to the position in question. In truth, that can wind up looking somewhat artificial, particularly if there’s no variety whatsoever (e.g., a data analyst who spends his free time reading statistics textbooks). Instead, be frank about it. Sports, creative activities, and musical instruments all add depth to the image of yourself as a candidate and help to bring your character to life in the eyes of the recruiter. 

Simple bullet points listing two or three of your main hobbies and interests are all that’s required here. Take care not to go overboard. 

How to Stand Out from the Crowd as a Data Analyst

For a position as a data analyst, candidates often assume they need to be dry, to the point, and plain, in keeping with what they see as the main requirements of the job. There’s also a tendency to over-focus on the hard skills while neglecting the value of high-level interpersonal ability. This attitude, which is surprisingly pervasive, gives you an excellent chance to stand out from the crowd by doing the opposite.

Your recruiter will probably have sifted through several other resumes by the time they get to yours, which means they’re probably tired of hearing about the technical side of the job. If you’re able to put more emphasis on the soft skills than the people you’re competing with for the position, you’ll be marking yourself out from the crowd in a powerful, immediate way. 

Being able to handle yourself in a collaborative environment is every bit as important as your statistical prowess. If you can demonstrate that you understand the importance of the softer skills, you’ll make an impression on the recruiter, which is the best thing you can hope for in a situation where it’s your resume against a pile of others.

What Makes a Data Analyst’s Resume Effective

An effective data analyst’s resume needs to pack a heavy informational punch in as few words as possible. The last thing you want is for your resume to seem bloated and verbose. Aim to fit it all into one page, and you won’t go too far wrong. 

Once you have a draft of your resume, take a break and come back to it later. Looking over what you’ve written with fresh eyes will reveal areas that could be improved upon. There’s no room for sentimentality when it comes to crafting an effective resume — you must be prepared to cut what you’ve already written over and over again.

Any information that seems superfluous needs to go. Your writing style must be as concise as possible, and paragraphs should be no longer than a few sentences. You’ll want it to be as short as possible without losing any important information; a 500-word resume with as much pertinent information as a 1000-word resume will always be more effective.

How Resumebuild Can Be Used to Craft the Perfect Data Analyst Resume

All that information might seem a little overwhelming, but there’s a fantastic tool out there you can use to make creating a strong data analyst resume a breeze. Resumebuild offers a resume building tool which allows its users to choose from a large number of slick, modern templates, each of which has been built on the foundations of best-practice industry guidelines and techniques

With thousands of pre-written samples built-in to the program to keep you on track, all you need to do is pick the perfect template for you, input your own personal touches, and download the finished product. Applying to your next dream job couldn’t be simpler.

More Job Descriptions for data analyst Resumes


data analyst

  • Gathered requirements for various data requests, designed and coded.
  • Produced support documentation and keep existing documentation up-to-date.
  • Carry out investigation of root cause analysis.
  • Provide technical support during software installation and configuration.
  • Developed unit test cases to test the mapping.

data analyst

  • Translate and compile large spreads of data
  • Recognize and identify quicker solutions
  • Writing simple scripts
  •  Organizing using excel

data analyst

  • Synthesize quantitative information to create powerful insights for executive decision-making
  • Prepare dynamic dashboards for multiple internal business departments by disseminating data from customers, marketing channels, sport logistics and financial reporting
  • Utilize Google Analytics to increase conversion sales and improve web user-experience
  • Attend monthly Sport Events as a member of the Customer Service and Visual Production Team

data analyst

  • Used machine learning to predict loan defaulters for retail bank in Python (backend)
  • Improved accuracy of prediction from 91% to 94% using external factors – home price index, land price index
  • Developed various models – support vector machine, decision tree, random forest, neural network
  • Certified by DataCamp for successful course completion on Data Science with Python

data analyst

  • Learning under the guidance of Client Services Team at Mercer
  • Updating client contrat information onto Mercer Client Participation databse used for distribution used of data collection material & survey results access granting to clients
  • Soliticiting participation from clients across markets in Asia for Mercer compensation survey
  • Working closely with market consultants regarding invoicing for clients for Mercer products sold across Asia
  • Providing analytical support in the form of data analysis to draw insightful solutions based on client responses