Due to a variety of things at work, there is potential for me to have a title change. This has led me to thinking about what title I would like to have and what would fit my position best.
Because of all of this, I am interested in what types of industries and positions are represented in the RStudio Community. What do you do and what do you use R for mainly.
As for me, I'm a Data Consultant (which could be changing) for a local mental health board. I do a lot of data entry, cleaning, and reporting as well as various other duties as assigned.
I've been a Data Analyst for most of my career, and then one day somebody who was familiar with my work suggested I change my title to Data Scientist, so I did. I've had discussions about whether or not I "deserve" the title several times, and I think that a lot of the job titles around data science are very vague and unclear. I think this is a worthwhile. It will be hard to achieve a critical mass around any specific rubric about job titles in the near future.
I work for an analytics consultancy. I work almost exclusively in R to do visualizations. I don't do MUCH modeling, but I do some and have the ability to do more than I'm asked to do currently in my job.
I've had the following titles, and used R for all of them:
Director, Data Science
Director, Data Analysis & Visualization
Outcomes Analyst
Data Analyst
Manager, Research & Evaluation
Director, Data System & Analysis (current position)
In every single one of the above positions I've done the following:
Oversee data collection
Data wrangling, cleaning, tidying
Data analysis ranging from simple descriptive statistics to more advanced statistical analysis to building predictive models
Data visualization as a means of communicating findings
Reporting: ad hoc and automated, written, verbal (lots of public speaking)
Training staff on introductory data analysis/statistics
Drive/participate in organizational strategic objectives
The only major difference is in the very last title, which includes a lot more IT-related work like determining organizational software systems and being responsible for more of the full stack (database creation, management, and administration)
Good question! I think because the 'data science' field is relatively new, there are a lot of people in roles that don't have a standardised job title yet.
I was hired with the standard 'data analyst' title but luckily more and more of my work is using R to develop visualisation tools and dashboards with shiny so I was thinking something like 'Data Analytics Developer' would be more apt.
One thing I've heard before that I am curious to hear opinions about: I've heard people say "You can't call yourself a data scientist unless most of your work is done in modeling." What do you think?
I think this hits the nail on the head perfectly. It also doesn't help that there are so many related jobs such as Data Architect or Data Engineer. It just adds to the confusion of trying to get a somewhat deeper idea of what someone actually does.
@rkahne As to your question, I think that it seems like a pretentious distinction. I think it is similar to saying something along the lines of "You can't call yourself a chemist unless most of your work is done in organic chemistry." If Data Science is going to be thought of as a science than it will have many subdisciplines, just like any other science.
I am the "Assessment and Quality Assurance Coordinator", but what I do on a daily basis is write R code I too wouldn't mind a title change, since while my work encompasses aspects of QA in Higher Education and Assessment my main responsibilities are eerily similar to what @jessemaegan lists, but confined to a single Faculty. I just wanted to work into my own title the significant role that I play in the data informed decision process.
Personally, I like the 'Data Consultant' title. I think that captures a broad range of activities and highlights the collaborative nature of the work that gets a little under-shadowed.
One thing Iāve heard before that I am curious to hear opinions about: Iāve heard people say āYou canāt call yourself a data scientist unless most of your work is done in modeling.ā What do you think?
I'm not surprised people say that, but I don't believe it's ultimately true. Building the actual model is just a fraction of the overall work that a data scientist does. Scientific reasoning begins with the data source and ends with the data story, imo. You can have multiple levels of touch along that spectrum of data science - and maybe that's where you differentiate roles - but whether you do or do not build models (imo) doesn't determine whether you are a data scientist.
Personally, I believe data analyst was simply the name for data scientist before people started coining that phrase. They do a ton of similar tasks - and the tasks that they do differently can likely be chalked up by demand at the employers who go with the analyst instead of the scientist name. Underneath, the capabilities are there for a data analyst to perform a scientific analysis of data, though.
I've had the following titles, and have done various degrees of sourcing, munging, modeling, and visualizing of data - and not all necessary in the same roles:
Manager, Data Science (current)
Lead Econometric Analyst
Commodity Analyst
I'm a straight up "statistician" (and engineer prior to that). But, I also have a degree in stats in an industry that has a regulator-enforced respect for statistics, so that probably makes sense. I would do a job search, find out what people are calling jobs that you're interested. See if you can get that title (or a "lower rank" version of it); it makes it a lot easier to work your way into one of those jobs down the road!
I go by Data Analyst. I work at a startup with a fairly small team so I end up working on tons of different things (a/b tests, dashboards, ad hoc analysis, some predictive modeling) so I think the vagueness of Data Analyst works nicely (plus should help me keep from being pigeonholed as we grow). I also transitioned to this from being the Director of Operations at my previous company after two years of online learning in the evenings, so I think having data analyst on my resume before deciding to specialize in another area (if that's what I end up deciding to do) will hopefully tell a nice personal development story.
My job title is "Environmental Engineer". However, while my work is definitely focused on that field, my day to day responsibility is ~90% using R for data analysis/visualizations/reporting/building shiny apps. So while i doubt i will change my job title officially, I consider what I do to be data science and myself to be both a data scientist and an environmental engineer
I like this idea for a thread and hate the misconceptions of what a "data scientist" is actually supposed to do.
My current title is "Data Scientist", but previously have held the titles:
Marketing Analyst
Management Analyst
Program Analyst
Senior Analyst
Reports Manager
I've done pretty much the same thing in all those roles...
combine data from different sources
clean up data manually entered by other people
create reports from data in databases
create plots to convey trends in data
analyze data to identify patterns useful for decision making
interpreting statistics and drawing inferences from data
using data to make predictions about the future (i.e. forecasting)
I have used machine learning to solve problems, but I don't feel like it's a requirement for the job or even the best way to solve all the problems presented to me. As far as tools, R is probably the one I use most frequently. I use it for most of what I do, e.g. data collection, data munging, ETL, data modeling, web scraping, analysis and statistical inference, data visualization, reporting (using R markdown) and web development (using Shiny). I also rely pretty heavily on SQL (various flavors, but mostly MySQL and BigQuery). In addition to R, I've written code professionally in Python and Go and academically using Scala.
I like to think of myself as pretty competent and capable as a professional data scientist, I've been told otherwise by plenty of people. I've been told that I'm not qualified to be a data scientist because:
I don't have a PhD
I haven't done enough machine learning in production
I don't use Spark (though I have tried using it from Scala, pyspark, Rspark and SparkSQL)
I don't know Java
I have too much experience doing data engineering/ETL
I have too much experience in management
I don't know enough math
I don't know what algorithm xyz is or how algorithm abc works
I don't use deep learning
Those excuses have made for some pretty awkward interview experiences, but I don't let it impact my confidence. I just keep solving problems and doing my best to provide real value to the company I work for.
I'm sorry that you've hit some of the excuses on that list. Many of them seem to be 'generic excuse x'. The "Don't have a PhD" is a very flimsy excuse, as experience in the field can be the equal (or better) of a PhD. It is an umbrella often encountered in academia and I'm sad to hear that it's got legs.
I encounter faculty members that give me excuses of a similar vein and I usually just smile and don't bother to correct them that I don't have a PhD/I'm not an Engineer/Don't know the demands of teaching in Higher Education (I know/have all of those things). I reflect on what they say, trying to shore up any deficiencies by learning on my own by taking every opportunity I can to work on something new.
And I carry on and do the same thing that you do, which is a true lesson in this thread. It doesn't matter what your title is, you should just:
It's been 5 years since I landed in the datasphere and I (or people around me) have used various names to call me so far:
social media editor (starting position)
social media consultant
marketing data analyst
data analyst
.
I don't call myself data scientist yet because I know I have a lot to learn to earn that name. Btw, my bachelor's degree is from Theatre Theory and Critics (my former boss studied aesthetics and philosophy), so if anyone says that you must have university degree from mathematics/PhD/other super mad math skills they are clearly underestimating the power of curious brain
Wow! I'm loving all of the responses and the diversity of R users that have found their way to being a part of this community!
@jakekaupp One of the main reasons that there is discussion of changing my title from "Data Consultant" is that is has caused extensive confusion on whether I am an employee or if I'm an outside entity contracted to do consulting work. T
@nick It's nice to see that there are some statisticians here, I thought that it was going to be one of the first titles that I saw mentioned given how closely related Data Science & Statistics are.
@scottbrenstuhl I'm very much leaning towards suggesting Data Analyst for this very reason. It's inherent vagueness can capture all of my job duties while still giving a general idea of what I actually do.
@raybuhr That stinks that there are people that feel a need to put others down because they don't fit into their box of what something should be. I think that it goes back to what I said earlier in the thread about there being many different specialties within every science that you can't do too little or too much of any one aspect of Data Science.
I'm a physicist. My work involves data and science. I switched to R for programming and data analysis about 10 years ago when a colleague showed me closures and plots (there was no going back to matlab).
Even now it seems an odd choice in many ways: I've never met another R user, and on the R side I'm seemingly the only person who cares about complex numbers in Rcpp. I guess Julia would be a more natural choice nowadays, but it may be a while before I can find the equivalent of knitr, ggplot2, (d)plyr.
My official title now is Manager, Business Intelligence. Which evolved from Business Intelligence Analyst after I got a few direct reports. Which evolved from Data Analyst(there wasn't much change in work. Just a way to praise me without giving more money, LOL). Before that, I was a Jr. Analyst, Decision Support, and before - straight operations, no data.
I've always been thinking that I'm not worthy of the Data Scientist title, but some latest Twitter discussions and @hadley's broad definition of Data Science made me think about talking about a title change along with taking my department to a new level with what we do.
I work for a very large household and commercial moving company, and we're in the infancy stage of data science.
I too wouldn't mind a title change, since while my work encompasses aspects of QA in Higher Education and Assessment my main responsibilities are eerily similar to what 
