Job Advice

Networking

Once you meet some professionals or even potential employers, ask them if they would be so kind and available to meet with you 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 traits they look for in ideal candidates in their field, and the current trends in their industries.

Be prepared with your questions, be professional in your interaction, respect their time, and express gratitude for their generosity.

Career Advising

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

Key skills that all statisticians need – for instance,

• explaining a concept clearly to non-statisticians

• writing a report with the statistical analysis plan, the results of the analysis, and their interpretation

• delivering a presentation to a large and diverse audience (i.e. public speaking)

• developing a rapport with people inside and outside of your team

• asking questions to clarify what a client wants to accomplish

• reading a client’s body language to sense repressed confusion or doubt, and taking the initiative to untangle their confusion and instill both clarity and confidence in their understanding

• contributing ideas, asking questions, and even disagreeing with others in meetings, phone calls and teleconferences (very different settings from one-on-one communication)

• writing emails in clear, unambiguous, flowing, professional and grammatically correct language

Advice Specifically for Statisticians

• Learn machine learning. Regardless of which field you are working in, machine learning has already become prolific in all areas of industry, and will become even more prolific in the near future.

• 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.

• Despite the emphasis on linear regression for modelling continuous response variables in statistics education, I have found that logistic regression is used a lot more often in industry. (I have worked in industrial statistics and medical statistics so far). Thus, develop a thorough understanding of the analysis of binary variables: 2-by-2 contingency tables, specificity, sensitivity, positive predictive value, negative predictive value, concordance statistics, ROC curves, chi-squared and Fisher’s exact tests of independence, and – most importantly – logistic regression.

• 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.

• Most employers who demand those skills will not consider candidates who lack them, (Some employers may give you some time to develop your skills in data manipulation for a little while, just because you need some time to learn about the data and become familiar with their intricacies and quirks.) identify the essential skills in your desired jobs and acquire them while you are still in school.

• In many statistics jobs, much of my work involved cleaning raw data, manipulating data into the appropriate formats, and merging different data sets to obtain the final data set that I need. Only after hours or even days of doing so can I even begin to use my statistical knowledge to analyze the data. Unfortunately, my academic education in statistics did not teach me skills in data manipulation and processing – the data sets in my homework were already cleaned and formatted for analysis. Some companies – like Predictum, where I first worked after graduating from my Master’s degree in statistics – compartmentalize these tasks so that the statisticians can focus purely on methodological research and analysis. Thus, I highly recommend you to learn how to manipulate data in both R and SAS.

#Readingnotes #Career #Stat