Let’s find out about Senior Data Scientists next.
The Senior Data Scientist — Reaching Level 2.
0The Senior Data Scientist has already worked as a Junior Data Scientist, Software Engineer, or completed a Ph.
He has 3–5 years of relevant experience in the field, writes reusable code, and builds resilient data pipelines in cloud environments.
Senior Data Scientists should be able to frame Data Science problems.
Good candidates have great insights from past Data Science experiences.
I also dig deeper into their ability to write production code.
— Kian KatanforooshCompanies prefer to hire Senior Data Scientists because they provide tremendous value at a reasonable salary.
They are more experienced than Junior Data Scientists, thus omitting costly greenhorn mistakes.
They are also not as expensive as Principal Data Scientists, while still being expected to deliver Data Science models in production.
It’s a very fun level to play, having surpassed Level 1.
0 and yet having room to grow to Level 3.
What they doThe Senior Data Scientist masters the art of putting mathematical models into production.
While Principal Data Scientists or Business Managers assign tasks, the Senior Data Scientist takes pride in building well-architected products.
He avoids logical flaws in the model, doubts systems that perform too well, and takes pride in preparing data correctly.
The Senior Data Scientist mentors Junior Data Scientists and answers business questions to management.
What they don't doThe Senior Data Scientist is not expected to lead entire teams.
It is not the responsibility of the Senior Data Scientist to come up with ideas for new products since they are generated by more experienced colleagues and managers.
While the Senior Data Scientist knows the details of the products they have built, they are not expected to know the overall architecture of all data-driven products.
The Level 2.
0 Data Scientist is skilled in statistics and better in engineering than a Level 1.
0 Data Scientist but strays away from the non-fun business part from Level 3.
A Senior Data Scientist has to be able to bring their code in production (with some support of the data engineers).
Seniors should be able to complete the projects they are given independently.
— Sébastien Foucaud, VP of Data Science at XingThe Senior Data Scientist is measured by the impact their models generate.
He has a good intuition about the inner workings of statistical models and how to implement them.
He is in the process of understanding the business of the company better but isn’t expected to provide solutions to business problems just yet.
Photo by Carl Raw on UnsplashNow that we’ve investigated Level 2.
0, let’s see what the final Level 3.
0 looks like.
The Principal Data Scientist — Endgame Level 3.
0The Principal Data Scientist is the most experienced member of a Data Science team.
She has 5+ years of experience and is well-versed in various types of Data Science models.
She knows the best practices when putting models to work.
She knows how to write code computationally efficient and is lurking around to find high-impact business projects.
In addition to her impeccable engineering skills and deep understanding of the scientific models used, she also firmly understands the business that her company works in.
She has a track record of impacting the business baseline with Data Science.
Principal Data Scientists need to have a very good understanding of the business problem they are solving before writing one line of code.
Meaning, they need to have the ability to validate ideas prior to implementation.
This approach increases the Data Science project success.
— Adnan Boz, AI Product Leader at eBayWhat they doThe Principal Data Scientist is responsible for creating high-impact Data Science projects.
In close coordination with stakeholders, she is responsible for leading a potentially cross-functional team in providing the best solution to a given problem.
Hence, her leadership skills have developed since Level 1.
0 and 2.
The Principal Data Scientist acts as a technical consultant to Product Managers from different departments.
With her vast experience and skills in the major Data Science categories, she becomes a highly valued asset to any project.
What they don't doWhile shaping the discussion about desired skills, it is not the responsibility of the Principal Data Scientist to recruit new team members.
Although she understands the business of her company and suggests impactful new products, the Product Managers are still responsible for market adoption.
She also leads teams, but career progression decisions are still taken by the team lead.
The Principal Data Scientist should steer projects independently from the Head of Data Science.
This person is expected to obtain first leadership skills and therefore it is important that this person communicates clearly, is empathetic, and has a good eye for people.
— Dat TranThe Principal Data Scientist has seen why products fail and thus she drives new projects successfully.
She is a valued contributor to product discussions and enjoys educating the company about Data Science.
With her experience in delivering impactful Data Science solutions, she is the most valuable asset in the Data Science department.
Photo by Val Vesa on UnsplashNow that you’ve seen the different expectations of a Data Scientist from Level 1.
0 to 2.
0 until 3.
0, let’s find out how you can use this knowledge to advance your career.
Leveling up your Data Science CareerIt doesn’t matter if you’re looking to enter the Data Science Career Game on Level 1.
0 or you’re looking to progress into a higher Level.
Take the following steps to make your next career move.
Evaluate your skillsAs a first step, compare your skills to the Data Science Skills Matrix.
How solid are your statistics skills?.How good are your engineering skills?.How business-savvy are you?2.
Plan your promotionMany companies have annual promotion cycles to advance their employees.
Julie Zhuo, VP of Product Design at Facebook, recommends as the first step to get promoted to make your aspirations to level up known.
State at the beginning of the progression cycle that you’d like to advance your career to your manager.
Ask your manager how she ranks your current skill set and what is expected to reach the next Data Science Career Level.
Improve your skillsOnce you’ve analyzed your skills and announced your desire for a promotion, it’s time to level up your skills.
Photo by Samuel Zeller on UnsplashDo you want to break into AI?.Then get rock solid about statistical models and learn how to solve problems with structured datasets.
Do you look to enter Level 3.
0?.Make sure you have your math, engineering, and business skills on lock.
Armed with these points, you’re well-prepared to negotiate the promotion with your manager.
Key TakeawaysNavigating the Data Science Career Levels is fun.
Remember the following Key Takeaways:Junior Data Scientists have good statistical skillsSenior Data Scientists excel at putting models into productionPrincipal Data Scientists know how to create business valueTo level up, evaluate your skills, announce your desire to progress, and work on outstanding skillsNuances exist within the different Data Science Career Levels.
For instance, Séb Foucaud is rather looking for strong engineering than math skills in Junior Data Scientists.
Some Senior Data Scientists might discover their passion for building scalable data pipelines and transition to a Data Engineering role.
Some Principal Data Scientists prefer to develop technical expertise while others rejoice in focusing on business skills.
Whatever career path you take, developing your skills around the three main areas of Data Science expertise will get you far.
This post continues the ongoing series of educating Data Scientists to become business-savvy.
The series aims at helping you polish your overall Data Science skill set.
If you enjoy the format, please follow me on LinkedIn or Medium to stay up to date with new articles.
Photo by Mirko Blicke on Unsplash.. More details