This lesson is all about getting a job as a data professional. In my mentoring work, this is the most common goal of people I mentor. People either want to start a data career, or move from one data role to another.
Finding a job is a (small) numbers game - because n is small, finding a job is high variance. Luck will play a big factor in your job searches.
It’s important to ride out this variance with a positive attitude - the belief that the right role is out there for you. See each iteration of CV fine tuning or interview as practice - it may take 50 applications to get one offer.
There are four steps to finding a data job:
You should always have a clear idea about which data role best aligns with your current work, your goals and your future work. Different data roles require different skill sets and career paths.
You can change your path - one common transition is for data analysts to transition into data scientists. At different points in your data career, different roles will best match your interests, skill sets and opportunities.
A data focus is important because it allows you to focus your development on the set of skills and technologies that matter, now or in the future.
The modern data world is vast, with many different areas to focus in. Understanding what you are focused on, either as an employee working as a data professional or someone working towards their first data role, is useful.
It’s important to reflect on what you want from your next role:
Another useful question to ask is “In my career thus far, when have I felt most happy and fulfilled? During those periods, what was my job/company like?”
A data focus allows you to focus your development on the set of skills and technologies that matter most to you in the short to medium term.
Understanding what you are focused on will help you excel as a data professional.
While at any one point it’s important to know which data career best aligns with your current work, it’s also important to know that this can change over time.
There is a lot of overlap between the competencies and skills in each of the three core data roles.
If you find yourself more attracted or useful in a different data role, change your focus!
A data professional provides business value from data.
As technology progresses, businesses generate and capture more and more data. Alongside the increased volume, variety and velocity of data, professions have developed to provide value from data.
What data professionals call themselves changes between companies and over time:
The business value a data professional generates can come in many forms, such as financial, ecological or health and safety.
What business value you want to generate, depends on who you work for and what work you do.
Any data project should generate enough value to justify it’s existence.
Many data professionals of today have education in other fields. Often they come from STEM backgrounds. Many more come from other backgrounds, such as commerce or the arts.
Historically, no university offered data science degrees or masters programs. Today this is changing, with many universities offering data science programs, and many data scientists now have formal education in data science.
I believe that anyone can start or transition into a data career.
The tools (such as Python) and knowledge (such as how backpropagation works) are all available online or through education - it’s only your ability to dedicate time and energy that limits you.
Some backgrounds (such as a math or programming heavy bachelors degree) will have an easier time picking up the skills and mindsets needed in modern data work.
While a STEM background offers an advantage, I believe that if you want it, anyone can develop the skills to deliver value as a data professional.
There are three core data roles:
These three roles are the foundation of modern data work. Deciding which role to focus on depends on your experience, skillset, goals and personality.
These roles are complementary, and it is not uncommon for professionals to develop skills across multiple roles over their career.
graph TD
core["Core Data Roles"]
analyst["Data Analyst"]
engineer["Data Engineer"]
scientist["Data Scientist"]
core --- analyst
core --- engineer
core --- scientist
A data analyst summarizes historical data.
These summaries of data are used to inform business decisions.
A data analyst uses statistics, a programming language like Python and SQL to extract insight from data. Communication is a key skill for the data analyst, required to communicate their findings to decision-makers.
A data analyst commonly requires SQL and Python skills. A data analyst will be expected to be able to work with tabular data, and is unlikely to require any machine learning knowledge.
A data scientist predicts unseen data.
Data science is a combination of predictive analytics with machine learning and experimentation design.
Data science requires a deep understanding of mathematics including calculus, linear algebra, statistics and probability. A data scientist will require good Python programming skills, and perhaps will benefit from SQL skills if their role involves querying existing SQL databases.
A data scientist could be expected to work with tabular data, images and text.
A data engineer provides access to data.
A data engineer is responsible for the infrastructure that stores, processes, and transforms data. They develop and maintain the systems that enable data analysts and scientists to do their jobs.
A data engineer requires SQL and Python skills, and will be expected to be comfortable working with cloud infrastructure.
In addition to our three foundational data roles, there are more derivative data roles.
graph LR
subgraph Core Data Roles
data_analyst[Data Analyst]
data_engineer[Data Engineer]
data_scientist[Data Scientist]
end
subgraph Derivative Data Roles
ml[Machine Learning Engineer]
ai[AI Researcher]
ba[Business Intelligence Analyst]
end
data_analyst
data_scientist
data_engineer
data_scientist --> ml
data_engineer --> ml
data_scientist --> ai
data_analyst --> ba
These derivative roles are built on top of the core data roles, and are often composites of the core data roles.
A machine learning engineer is responsible for developing and deploying machine learning models.
They work closely with data scientists to design and build machine learning models, and then work to deploy those models into production. The discipline of deploying and managing machine learning in production is known as MLOps (for Machine Learning Operations).
A machine learning engineer will require good programming skills, a deep understanding of machine learning and the ability to work with cloud infrastructure.
A business intelligence analyst works to understand the needs of a business and its stakeholders, and to identify opportunities for improvement.
They use data analysis and modelling to identify trends, patterns and insights that can inform business decisions. They work closely with stakeholders to understand their needs and to develop solutions that address those needs.
They may also work on identifying new opportunities for revenue and growth, and on developing and implementing strategies to achieve those goals.
The AI (Artificial Intelligence) researcher is responsible for advancing the field of artificial intelligence through research and development.
They work on developing new algorithms and models for machine learning, natural language processing, computer vision, robotics, and other areas of AI.
They may also work on developing new applications for AI technology, such as autonomous vehicles, virtual assistants, and medical diagnosis tools.
AI researchers require a deep understanding of mathematics, computer science, and statistics, as well as experience in programming and data analysis.
Favour a concentrated, deep approach over a wide, shallow net. You only need to find one job! Better to spend half a day writing materials for one job than four.
Your career materials will reflect the job requirements of your desired role.
When you review job advertisements for roles in your data focus, you are looking for skills, tools and competencies like leadership or initiative. If they are mentioned in the job advertisement, they should be mentioned in your career materials (CV, cover letter etc.).
One of the best places to find a job is directly from a company - by paying attention to their job listings on their website or social media.
If you know someone, you can be referred or introduced. These direct contact channels are difficult to find, and valuable if you have them.
More common is to find job listings on an aggregated platform such as LinkedIn, Glassdoor or Kaggle Jobs. Most of these sites allow you to setup job alerts based on keywords or to follow companies you like.
Even when you find a job on an aggregation site, always try to apply through the company website - it is very easy to spam applications on LinkedIn!
Jobs can also posted on community forums such as Slack or Discord channels - here are a few of our favourites are the Climate Change AI and DataTalks.Club.
Getting a job requires demonstrating competence - showing you can do the job.
Demonstrating competence means explaining why you’re a good fit - communicating the value you would bring to the company in the short and long term. This is an opportunity to show understanding of the company goals.
Competence is best demonstrated through stories - concrete examples that show your capabilities.
Compare the difference between two approaches to communicate skills.
The first approach to communicate skills lists the skills:
Skills:
- data analysis,
- Python.The second approach to communicate skills tells a story:
Used Python to analyze data.These feel different - the story implies action, the list bores. Stories also feel more real & convincing - it’s harder to lie when you are painting a picture.
Action focused stories highlight your contribution to tangible successes, such as developed a machine learning model that reduced churn 4% or improved data quality leading to saving 4 hours of developer time per week.
Use strong action verbs to describe your experience - take credit and responsibility for your work (while remaining honest). Be specific - include numbers in your accomplishments whenever possible.
Show evidence of your successes - use specific and relevant examples of your achievements to demonstrate you have what the employer is looking for.
Stories can be reused - on your CV and in an interview. One story can also communicate multiple skills or competencies - such as showing teamwork and leadership.
To summarize, stories are important because they:
There are four competencies that make up a data professional:
graph TD core[Core Data Skills] domain[Domain Expertise] prog[Software Engineering] analysis[Analytics] soft[Soft Skills] core --> domain core --> prog core --> analysis core --> soft
One advantage that someone from any background can bring into a data career is domain knowledge and expertise.
If you have worked in a domain like energy, fashion or law, the understanding you have gives you an important advantage as a data professional.
Most data professionals use code in their work.
Code offers a number of advantages over other analytical tools like Excel:
While not every data professional needs to be able to work as a software engineer, software engineering skills are important for data professionals.
The table below provides a general guideline of the importance of each skill for each role on a scale from 1 to 3, with 3 being the highest.
| Data Scientist | Data Analyst | Data Engineer | |
|---|---|---|---|
| Programming | 2 | 1 | 2 |
| Git | 2 | 1 | 3 |
| Shell | 2 | 1 | 3 |
| SQL | 2 | 3 | 3 |
| Cloud | 2 | 1 | 3 |
| Databases | 1 | 3 | 3 |
| Pipelines | 1 | 1 | 3 |
| CI/CD | 2 | 1 | 3 |
Mathematical understanding is core to working as a data professional.
This includes understanding things like:
Which specific areas of analytics are important depends on the role you are working in.
Some data roles (such as data science) require a deep understanding of machine learning, while others (such as a data engineer) may not need any machine learning at all.
The table below provides a general guideline of the importance of each skill for each role on a scale from 1 to 3, with 3 being the highest.
| Skill | Data Scientist | Data Analyst | Data Engineer |
|---|---|---|---|
| Probability | 3 | 2 | 1 |
| Linear Algebra | 3 | 1 | 1 |
| Statistics | 3 | 2 | 2 |
| Data Visualization | 2 | 3 | 1 |
| Machine Learning | 3 | 1 | 1 |
| Data Interpretation | 2 | 3 | 1 |
| Reporting | 2 | 3 | 2 |
| Experiment Design | 3 | 2 | 1 |
Alongside the hard skills discussed above, a data professional also needs soft skills such as:
The table below provides a general guideline of the importance of each skill for each role on a scale from 1 to 3, with 3 being the highest.
| Skill | Data Scientist | Data Analyst | Data Engineer |
|---|---|---|---|
| Communication | 2 | 3 | 1 |
| Collaboration | 2 | 2 | 3 |
| Time Management | 3 | 3 | 3 |
| Curiosity | 3 | 3 | 1 |
| Attention to Detail | 3 | 3 | 3 |
Career materials are content that you to create & maintain to succeed in your career. This content will be shared with others, and can represent you both privately and publicly.
Your career materials should be tailored to the data role you are interested in. They should be full of stories and examples of you demonstrating competency in the skills your prospective employer cares about.
All your career materials should be cleanly and consistently formatted, without spelling or grammatical errors.
A CV (Curriculum Vitae) summarizes your education, work experience, and skills.
It’s likely that everyone involved in your job application process will look at your CV - it’s the most important career document.
You should share your CV as a PDF document, and it should be one or two pages long.
Space on your CV is valuable - it represents time that someone needs to spend understanding your CV. The more you can focus on important, relevant skills, the better your CV will be.
Personally I will always want a single page CV. Two pages is also common.
A CV should have these sections:
You should not have a skills section - instead your skills should naturally appear in your work experience, education or projects sections.
The first part of your CV should be a header that contains:
Whether you need to include a picture or not depends on the market - most likely you don’t need a picture on your CV.
Adam Green - Christchurch, New Zealand - adam.green@adgefficiency.comYour CV should start with a one to three sentence summary.
Your summary can focus on your experience, your skills or your professional mission.
Data {analyst | scientist | engineer} with {n} years of experience in {industry}.
Proven ability to {skill}.
Passionate about {industry | topic}.Your work experience should be based on stories - action focused, using phrases like:
{job title} at {company} from {date} to {date}
- {action focused achievement},
- {action focused achievement}.Your education section lists relevant education at universities or bootcamps.
You can also consider adding online courses or certifications.
How much you include here depends on your level. If you are early on in your career, you might include a relevant list of courses for your degrees.
For those with a decade of experience, they may only include the degree title, preferring to use the space on their experience.
{degree} at {university} from {date} to {date}
- {action focused achievement},
- {action focused achievement}.Skills and projects are optional sections.
Skills should be tailored the position, and match what they ask for in the job advertisement.
While a list of skills may be good for word recognition, it is too easy to lie on skill lists. So much that they are often ignored. Stories however, are much harder to fabricate. Be specific!
If you do include a skill on your CV, be prepared to be asked questions about in during interviews.
A cover letter provides a more detailed explanation of why you are a good fit for the job.
A cover letter is a one page personalised note to support your job application. Like your CV it should be targeted, tailored and personalised. It should be formatted consistently with your CV. Your cover letter should be one page - between five to eight paragraphs. Ideally it is addressed to specific person.
The goal of a cover letter is expand on your CV - not to repeat it. A cover letter lets you expand on things like your current availability, a story of your recent successes or examples of benefit you can bring to a role.
A example cover letter structure:
To {whom it may concern | $NAME},
I am writing to apply for the {POSITION} role at {COMPANY}, as advertised on {WHEREYOUFOUNDIT}
First paragraph:
- be clear about which position you are applying for and how you found it,
- outline why you are applying,
- your main experience.
Middle section (second, third and/or fourth paragraphs):
- how your skills and experience match the job,
- what bring to the role (show skill alignment), suitable skills
- key achievements, background on what makes you relevant,
- why you want to work there,
- each paragraph can be one story.
Ending - one of:
- I look forward to hearing from you to discuss this role further,
- Kind regards,
- Your sincerly,
- Thank you for considering my application. I look forward to hearing from you,LinkedIn is a professional social media website. It allows you to showcase your skills, experience, and qualifications. It also allows you to connect with other professionals in your field and get an idea of the kind of job opportunities that are available.
For better or worse, LinkedIn is currently the leading professional social media website. You don’t need to be a LinkedIn influencer - but you should keep your profile updated and professional.
Your LinkedIn profile is most similar to your CV - both can have the same content. A LinkedIn profile has no restriction on length - think of it as an expanded, public version of your CV.
GitHub is a platform where developers can share code. It gives an employer a sense of your skills and experience in programming, and can help you to stand out in a crowded job market.
A GitHub profile has many false negatives - many good developers have no GitHub, with their best work being done in private & on the job.
A well-crafted portfolio of data projects is one of the most powerful ways to demonstrate your skills to potential employers. While
your resume tells employers what you can do, your portfolio shows them.
Portfolio projects serve multiple purposes in your job search:
Choose projects that align with your target role. Your portfolio should contain 3-5 quality projects that collectively demonstrate the key competencies needed for your target position.
| Role | Project Focus Areas |
|---|---|
| Data Analyst | Data cleaning, exploratory analysis, visualization, business insights, dashboard creation |
| Data Scientist | Predictive modeling, machine learning, statistical analysis, A/B testing, natural language processing |
| Data Engineer | Data pipelines, database design, ETL processes, data architecture, scalability solutions |
A few well-executed, thoroughly documented projects are far more valuable than many incomplete or superficial ones. For each project, focus on:
For guidance on creating high-quality data science projects, refer to our Data Science Project Checklist. This resource provides a comprehensive framework for ensuring your projects are:
Present your portfolio in a way that’s accessible and professional:
flowchart LR app(Application Process) --> ap(Apply via Website) ap --> it(Interviews) it --> ta(Take Home Test) ta --> jo(Job Offer) ;
Throughout the application process, it’s important to demonstrate interest and engagement in the company and their problems. You should always respond promptly throughout the application process.
Three main parties could be involved - a recruiter, a hiring manager and potential co-workers.
The recruiter is responsible for finding candidates and passing them onto the hiring manager.
The hiring manager is responsible for determining whether a candidate gets a role.
Potential co-workers will be involved at different points in the process, often during interviews or doing technical assessments.
The process starts with the candidate applying via a company website, which can be done by submitting a CV, cover letter and other required application materials. Always prefer applying via the company website over an aggregator like LinkedIn.
Apply for jobs that resemble your skillset and your data focus (such as data engineering) - avoid applying to jobs where the core requirements are clearly unrelated your primary skill set.
Go for jobs one step above (and at!) your level - many companies advertise for a senior data scientist, but will hire a data scientist.
Job description has two parts - what you’ll do and what the company is looking for (experience & education).
Qualifications (like experience or education) are always negotiable and should never deter you from applying if you think you’re almost there but missing a few requirements.
Why you are a good fit - ask yourself the following questions:
After submitting the application, the candidate will typically move on to the interview stage, where they will have the opportunity to speak with the hiring manager or a member of the HR team.
During the interview stage, the candidate will have the opportunity to provide more information about their qualifications and experience, and to ask any questions they may have about the job or the company.
Some of the basics for interviews are:
Don’t be too modest - avoid one word answers, take the time to expand on your skills, abilities, achievements and goals.
Always answer questions - if you need to take a minute to answer with confidence, ask for a moment.
You should be able to answer questions about:
Types of questions you may be asked include:
how would you design this feature.Behavioral or competency questions typically follow the format “Tell me about a time you did X” - where X relates to either a soft skill or technical competency. Being prepared for these questions is crucial for data job interviews.
Problem-Solving & Analytical Skills:
Time Management & Prioritization:
Leadership & Teamwork:
Adaptability & Learning:
Initiative & Innovation:
Communication & Stakeholder Management:
Preparing stories for these questions in advance using the STAR method (Situation, Task, Action, Result) will help you provide structured, compelling responses during interviews.
Always ask thoughtful questions during interviews - this demonstrates interest and helps you evaluate if the role is right for you. Below are categories of questions you might consider asking.
Team Structure & Culture:
Technical Environment:
Work Management:
Career Development:
Performance & Success:
Management & Feedback:
Company Values & Priorities:
For Startups:
For Large Corporations:
Part of the application process may involve live, in person programming.
A good way to think about these is:
After the interview, the company may ask the candidate to take a take-home test as an additional assessment of their skills and qualifications. This could be a case study, a sample project or a test that is relevant to the position.
Some tips for take home tests:
Sample ML take home pipeline:
Once the assessment is completed, the company will make a job offer to the candidate if they are deemed to be a good fit for the position.