(This article was originally published at The Chemical Statistician » Statistics, and syndicated at StatsBlogs.)
Introduction
A graduate student in statistics recently asked me for advice on how to find a job in our industry. I’m happy to share my advice about this, and I hope that my advice can help you to find a satisfying job and develop an enjoyable career. My perspectives would be most useful to students and recent graduates because of my similar but unique background; I graduated only 1.5 years ago from my Master’s degree in statistics at the University of Toronto, and I volunteered as a career advisor at Simon Fraser University during my Bachelor’s degree. My advice will reflect my experience in finding a job in Toronto, but you can probably find parallels in your own city.
Choosing Your City of Study
Most good statistics jobs that I have encountered require at least a Master’s degree. (However, many employers in data science are happy to hire anybody with a strong background in math and computer programming and experience in working with data. This advice post will reflect my experience in working in statistics, but I encourage you to research working in data science to broaden your scope.) When students choose where to study for their graduate degree, I think that the industrial activity and job market for statistics in the city of study should be a key factor in your decision. When I was choosing my graduate school for my Master’s degree, I was offered to study with a world-renown statistician who specializes in machine learning, but his university is in a city without a lot of industrial activity for statisticians. I ultimate chose to study in Toronto mainly because of the high quality of the statistics department at the University of Toronto (there are some world-renown professors there), but also because of the high industrial activity for statisticians in that city. (The course-based Master’s program has an intense but short duration of 8 months, which was another attraction for me. Had I wanted to extend my degree by pursuing a thesis or a thesis-like project, the program would have gladly accommodated that, so it was nice to have that option to pursue the more traditional 2-year path with a thesis.) It is harder to find a job in statistics in a smaller or less industrious city like Vancouver, where I currently work. Some of the advice that I offer below would be difficult to implement if you live in a city with little industrial activity or a small job market for statisticians, but you can still do some of it via the Internet through email, Skype and Google Hangouts.
Networking
The best way to find a job is via networking. Employers may post jobs online, but they prefer to hire good candidates whom they have personally met in the past in professional settings or via trusted referrals from their colleagues – it’s faster and far less time-consuming than examining hundreds of cover letters and résumés from strangers. I have even encountered many companies that are not actively seeking new employees but are eager to hire smart and hard-working people and find a role for them to fit into.
Networking can be done face-to-face and online, but face-to-face is much better. Your network starts with your professional and academic acquaintances: professors, teaching assistants, classmates, past employers, past co-workers, and colleagues from extra-curricular activities. I did not have a single professional contact in Toronto when I first arrived there, so I had to grow my network from scratch. Attend industrial events to meet professionals in statistics and analytics, and you may be talking to you future employer. This was how I found my first job in statistics after my graduate studies. Be proactive in approaching strangers at these meetings and ask them about their work. If someone’s work especially appeals to your interests, express your feedback and enthusiasm to them, and perhaps even share your thoughts or ideas. (Remember: Networking works best when it’s a win-win interaction. Be prepared to both learn from someone and share what you know.) During my graduate studies in Toronto, the SAS User Groups in Toronto were the best networking events. These meetings are also great ways to improve your skills in using all types of SAS products, from Base SAS to SAS Enterprise Miner. Toronto’s SAS community is especially active, and I attended the meetings held by
the Toronto Area SAS Society (TASS)
the Toronto Data Mining Forum
the SAS Health Users Group (HUG)
The above web pages contain great archives of past presentations about SAS, and they are great learning resources! I also attended some smaller but still very valuable events like
the Business Analytics Seminar Series held by the Southern Ontario Regional Association (SORA) of the Statistical Society of Canada (SSC)
The American equivalent of the SSC is the American Statistical Association, which has a great web page with data on salaries that you can use for your salary negotiation.
the Toronto Applied Biostatistics Association (TABA)
Look for groups in data science, machine learning, statistics, and biostatistics in Meetup. Since moving to Vancouver recently, I have found some very active groups in data science and analytics.
Information Interviews
Once you meet some professionals or even potential employers, ask them if they would be so kind and available to meet you with for an information interview. These meetings are not about actual job opportunities; they are a chance for you to learn more about that person’s work, company, or industry. Ask them about what they do, what they like/dislike about their job, how they got the job, what qualities they look for in ideal candidates in their field, and the current trends in their industries.
Of course, this is an excellent time for you to make a great first impression, because your interviewee may be your next employer or may refer you to your next employer. Be prepared with your questions, be professional in your interaction, respect their time, and express gratitude for their generosity.
Career Advising
Visit your university’s career centre to devise a plan on how you will find a job. (If you are a recent graduate, you may still be able to access those services as a recent alumni – check your career centre’s rules. I was able to use the University of Toronto’s Career Centre‘s services for 2 years after my graduation.) Meet with a career advisor and be specific about what you want to get out of the appointment. Your first meeting may be to discuss your overall goals and aspirations. The career advisor may point you toward general directions and suggest ways for you to improve upon any weaknesses in your approach or your qualifications. (Even after volunteering as a career advisor for 6 years, I still learned a lot from my appointments with my own career advisor. Those meetings were fruitful because I was quick to identify my weaknesses and prepared for each meeting with specific questions about how to work on my deficiencies.) As you go through the different stages of your job search (searching, applying, interviewing, negotiating, working, moving onto a new job), continue to meet with an advisor to work on all areas of your career development:
networking
information interviews
cover letters
résumés
job interviews
salary negotiations
researching potential employers
developing a good online presence
choosing and preparing your referees
showing that you are a good fit with your desired potential employer
phone interviews
Searching for Job Postings
Even the traditional method of looking for job postings has been advanced in recent years – and much of it is due to machine learning! LinkedIn and Indeed are my favourite job search web sites, but there may very well be others that are good. Good job search web sites use recommender systems to automatically find jobs and employers that suit your interests, and they will send regular alerts to you via email about new job postings.
Communication Skills
Develop your communication and interpersonal skills. Regardless of how technical your job may be, you will need to communicate and relate with your boss, co-workers, and clients, and much of that communication involves empathy, active listening, sensitivity, assertiveness, negotiation, compassion, setting boundaries, expressing anger professionally, expressing gratitude professionally, providing constructive feedback, accepting positive and negative criticism, and diplomacy – none of which you will learn in statistics classes. These skills can only be developed through experience, and getting a lot of good work experience or volunteer experience (in statistics or non-statistics roles) is a great way to do so.
Another great way to develop communication skills for statistics is by taking a statistical consulting course, which many graduate programs in statistics offer. These courses will pair you with actual clients who seek your advice and expertise, and they are often non-statisticians who need you to communiate technical concepts in ways that are understandable and practical for them. I took such a course at the University of Toronto during my Master’s degree, and it was a valuable experience for me.
(I offer this following advice as an anglophone working in North America. Please feel free to change the word “English” to whichever language is dominant in your place of work.) Many statistics students do not speak English as their first language. If this is true for you, I highly recommend you to take the time to develop your English skills in social settings outside of statistics and away from the classroom. Read properly written English closely and learn to mimic correct grammar until you grasp the rules yourself. Train your accent to become closer to that of your native English-speaking peers. I have met many smart and hard-working statistics students whose talents are not fully realized in the working world because they are hesitant to socialize with people who don’t speak their native tongue; while this is easier for making friends, it also greatly limits your ability to develop your English skills and familiarize with the local culture. Try to socialize with anglophone people; a friendly person with mature wisom and a kind heart will shine through any language barrier with like-minded and like-hearted people.
Advice Specifically for Statisticians
Learn both SAS and R. Academic statistics tends to use R, but many statistics jobs require good knowledge of SAS. There are many good resources to learn both languages online. My blog offers many resources on R programming.
Learn machine learning. Regardless of which field you are working in, machine learning has already become prolific in many fields, and will become even more prolific in the near future. If you haven’t learned any machine learning in school, have no fear – there are some great resources online for free! Many massive open online courses (MOOCs), like Coursera, teach machine learning, and most include programming-intensive projects and assignments that you can later display to your potential employers as evidence of your skills. There is a great Youtube channel by Mathematical Monk on machine learning that I also like.
Most biostatistics jobs that I have seen require knowledge of survival analysis as a basic requirement. They also require knowledge of how to use survival analysis in SAS. I have really enjoyed reading Paul Allison’s textbook, Survival Analysis Using SAS: A Practical Guide, to learn both.
In biostatistics, knowledge of statistical genetics is becoming increasingly more valuable. If you want to work in biostatistics, being good at it will certainly give you an edge.
The American Statistical Association has a great web page with data on salaries that you can use for your salary negotiation.
Take the initiative to show samples of your work, even if most employers don’t explicitly ask for them. It’s a great way for you to concretely demonstrate your technical and theoretical knowledge. For example, I once brought a MATLAB script implementing a recommender system to a company to demonstrate my passion for machine learning, collaborative filtering, and computer programming. I also brought two 1-page descriptions of what I did: one in words, and one using a flow chart.
An even better way to show your work is to write a blog. I will write another advice blog post about that; in the meanwhile, check out my blog or the blogs in my blog roll for some good examples.
Many entry-level jobs in statistics require a lot of data manipulation and data processing before any statistical analysis can be done. Learn to do this well with both SAS and R. Many jobs also require good knowledge of SQL. Learning Python would be a great asset, and that’s what I’m currently learning.
Filed under: Advice, Statistics, Statistics in Industry and Practice Tagged: biostatistics, career, career advice, career development, communication, communication skills, Coursera, data manipulation, data processing, information interview, interpersonal skills, jobs, machine learning, massive open online courses, MOOC, networking, SAS User Groups, sas users groups, statistical genetics, survival analysis, TASS, toronto, Toronto Area SAS Society, working in statistics 
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