Job prospects with Masters in Applied Mathematics

[MathWorry]

I am at starting to become increasingly worried and I need help/advice/what ever you can give me.

My major is mathematics, I will be getting a BS in it next year. I have an interest in Analysis and Probability and will be taking a years sequence in Integration and Measure that year.

My original goal was to get an MS(maybe a PhD) in Applied Mathematics.

I have now spent some time researching jobs and I am starting to become afraid. I always knew a BS in Mathematics wouldn't offer a lot of options but I had always thought an MS would get me in the door in industry(if the PhD fell through). Now I don't know anymore.

Anyone have any thoughts? What is the best area in mathematics for employment? Is an MS in applied Mathematics useable as far as careers go? What are the job prospects? - Excluding Actuary what is there?

Secondly, what can I do to best outfit myself, making an MS degree in Mathematics more attractive?

 

[Locrian]

Learn some SAS & SQL and look into data jobs. Learn some data science and look into data science jobs.

And frownyface at your exclusion.

 

[MarneMath]

I have a masters in Statistics, so slightly different background. Currently I'm a data scientist and I can give you some tips if you intend to follow this career path.

1. Learn Python, R, SQL, Hadoop, HBASE, Scala (if you're hardcore!). Sounds daunting? Maybe, but everything comes in time.
2. Focus on Machine learning and statistical learn. That means know your clustering, regressions, preprocessing and linear algebra like you know your name.
3. Technical is good, but soft skills are important. Ensure your resume states you have the ability to communicate technical detail to laymen. Majority of my job is selling my team's ideas to a business partner.
4. Get involved in kaggle. Even if you place dead last, the time spent learning how to munge data, build a model, and select variables gives you a huge talking point over other people.
5. Be familiar with git, linux, unix, agile methodology of dev, vm's, sparks.
6. Optimization! Not necessary but someone who can derive arg min for a log likelihood function makes me do less work so yay.
7. Parallel processing and mapreduce. Not strictly necessarily, but definitely makes you stand out. Especially if you can explain to me why Sparks is different than hadoop

Sounds like a lot of computer science? It is. However, you don't need to be at the level where you can backend engineer the systems and do all that fun stuff. Familiarity with the ideas and having a basic idea of how things flow is good for someone on the analytics side. What's important is taking the time during your masters to develop a good procedure behind model building. A lot of that includes non math work like munging data into a usable format. Other parts include heavily statistical frameworks like feature selection and clustering. There's other useful math you can throw in there depending on the domain that excites you. For example, I do a lot of signal processing (and for some reason image/text processing) so I learned a lot of electrical engineering on my own to help me understand what the heck I was dealing with.

Moving. Many of my peers have masters. A lot also has PhD. The key is finding someone who is flexible, good at business relations, and able to move from different problems quickly and efficiently.

Job prospects, pretty good. A lot of companies right now are hiring us, but a lot of companies don't know what to do with us. It's really tricky to land a good first role, that isn't using data science as a new term for data analytics. If the positions ask you to be an expert on teradata and prepare reports, be weary! If the position ask for machine learning, model building, and communication skills, be happy!

Hope this helps!

 

[FactChecker]

Everything has some random behavior (statistics), some feedback (control laws), some optimization, and doing something with that requires computer programming (look at MATLAB/SIMULINK). That might sound overwhelming, but no one expects you to be expert at all of it. Just be aware and adaptable. You can be very employable.