Introduction: Careers in Data Science
Generally speaking, data scientists work with data sets that are too large to be meaningfully analyzed by traditional statistical methods and analytical tools. (These huge data sets are often referred to as simply ‘big data’.) As it has become ever easier and more economical for firms in a variety of industries to collect and organize massive amounts of data, the demand for professionals able to turn that data into actionable business insights has grown rapidly. And this growth shows no sign of stopping.
Data science is now used in just about every industry with the means to generate big data, including: finance, consulting, retail, manufacturing, international trade, government, healthcare, research institutions, non profits, telecommunications, agriculture, gaming, and many more.
Data Science or Data Analytics? These terms are often used interchangeably. Individual positions may list either of two job titles. And for all intents and purposes these roles all use the same set of tools and require many of the same professional qualifications, with data ‘scientists’ at one firm doing much the same work as data ‘analysts’ in another.
For simplicity’s sake, we’re going to stick with data science for this article.
As a job seeker, you should identify which terms your target employer prefers and tune your application materials to fit. It’s perfectly acceptable to tweak your job titles on your resume to better fit the expectations and needs of prospective employers.
This does take a bit of finesse, however. So unless you are confident in your resume-writing skills, we suggest having a career advisor tune your resume.
Breaking Into Data Science: Qualifications
Data scientists must be highly analytical computer whizzes with outstanding programming knowledge and exceptional mathematical skills. Attention to detail is key, as are strong problem-solving skills. And all data scientists must be extremely organized.
Impressive industry growth and good pay means that competition can be fierce. Fortunately, the diversity of companies, including many forward-thinking firms, means that candidates with a wide array of backgrounds can be successful.
A degree from an elite university is not nearly as important as being able to show that you are a highly skilled programmer. Specialized degrees in subjects such as computer science, business information systems, economics, mathematics, or statistics can help but are not required. And certifications are rarely required.
Additionally, since the data science work being done at research universities is often as advanced as that being done in the private sector, students who take advantage of research opportunities at their schools can build compelling resumes without private sector experience. But don’t take this as an excuse not to seek out internships and other opportunities in private sector. They will always put you at an advantage!
As with any technical role, required skills and tools vary from job to job. Nonetheless, an aspiring data scientist preparing to enter the job market should aim for a strong foundation of specific skills that are widely applicable. These include:
- Software engineering fundamentals
- Multivariate calculus and linear algebra
- Understanding of relational databased such as MS Access
- Understanding of SQL and similar databases
- Understanding of data analysis, modeling, cleansing, and enrichment
- Ability to develop and document procedures and workflows
- Ability to carry out data quality control, validation, and linkage
- Ability to represent data and findings through graphics and visualizations (A great way to make a powerful first impression!)
- Hadoop analytics
- Fluency in the most popular programming languages for data science, such as: Python, R, and MATLAB, C/C, and Java
- Knowledge (or at least awareness) of industry-specific databases and data sets (Know this before going to your first interview!)
- Familiarity with industry-specific tools and platforms, such as: Google Analytics, SEO, Tableau, QlikView, Crystal Reports, Alteryx, D3, SPSS, SAS, RapidMiner, etc.
In addition to technical know-how, data scientists must be excellent communicators. The insights data science generates originate from complex algorithms applied to otherwise impenetrable data sets. Especially for smaller firms, a data scientist may be the only person on the team with a clear understanding of where the insights came from and why they make sense based on the data. This makes it all the more important that they are able to convey and back up their findings clearly and persuasively to decision makers and stakeholders, who may have little or no understanding of data science itself.
Further Advancement: Managing Teams & Becoming a Principal
Associate consultants work on case teams for two to three years, at which point they may be given teams of their own to manage, supporting principals on specific projects. In another two to three years they can become principals themselves.
Principals work much more closely with clients and also interact with prospective clients in order to sell the consulting firm’s services. Since a management consultancy’s ‘product’ is so closely tied with the expertise embodied in its high level consultants, and since the services the firm can offer will be completely unique, tailored to fit each client’s situation, a consultancy’s best salespeople are its most senior employees. Principals and partners have the credibility and expertise to provide initial recommendations that are robust and intelligent, and that will gain the trust of potential clients.
Quick Tip: Do Your Homework Before Applying!
It is always a good idea to research the firm you are applying to. This is especially the case if you are going for a position at one of the big names such as McKinsey, BCG, Bain, Deloitte, etc. Do your research and understand what makes the firm you are applying to different from the others. They each have a unique industry focus, strategic approach, and company culture. These giants receive thousands of applications from recent graduates each year. Most applicants would be happy to work for any one of these prestigious firms; and this can show in applications that are too general. Even if you would be equally happy to work at any of them, in your individual applications you should treat each one as if it is your first choice. There is more to this than adding a one or two unique lines in your cover letter.
Breaking Into Data Science: Career Paths
Data science is a combination of software engineering, coding, mathematics, statistics, design, and traditional scientific research methods. And since data science positions vary widely, there is no standard career path.
Aspiring data scientists who demonstrate exceptional ability can secure jobs and quickly rise in the industry even if they do not hold advanced degrees.
Small firms and tech startups hire from a wider pool of candidates, basing their decisions more strongly on each candidates’ demonstrated abilities. Larger and more traditional firms that have only recently added data scientists to their teams receive many applications and are more likely to screen out candidates without advanced degrees from prestigious schools.
Career advancement for data scientists can take a variety of forms. And successful data scientists have considerable freedom to advance, make lateral moves, or strike out on their own, depending on their lifestyle preferences and professional goals.
The strongest communicators may transition into leadership roles, stepping away from the ‘nuts and bolts’ of data science and instead leading teams, communicating findings to clients and stake holders, or, if they work at a consultancy, selling their firm’s services.
Conversely, advanced data scientists who prefer the technical side of things can find significant freedom to hone their craft by innovating data science-based products in the private sector, or by seeking positions at research universities. These individuals can become highly sought after super-specialists, publishing in academic journals and/or consulting on their area of expertise in the private sector.
Continuing Professional Development
Regardless of what career path you choose, one thing remains true for all data scientists: the importance of continuing professional development. Data scientists are constantly pushing the field’s cutting edge further ahead. Those who find themselves in leadership roles need to know what the newest tools and techniques are in order to make the best decisions for their team and their firm. And the most ambitious young data scientists will want to know what is just around the corner, so that when their firm implements a new tool, or when the their client asks about a new technique, they will stand ready to demonstrate their unique expertise and foresight.
There is also no standard expectation for data scientists when it comes to lifestyle and working hours. Instead, industry-to-industry norms should guide your expectations. For example, large financial firms and management consultancies will expect you to work long hours in-office as an integral part of their teams, while start ups are likely to focus more on results and less on attendance. And while most data science positions are salaried, for experienced and/or ambitious data scientists, there are many opportunities to work-from-home on contract-based projects with small firms.
Salaries for data science jobs vary widely based on experience, industry, and location. (Though as a rule, private sector jobs pay more than positions at research institutions.) Generally, an entry level data scientist can expect to make around $28,000 yearly, rising to between $35,000 and $45,000 per year as experience is gained. And senior data scientists can expect to make more than $60,000 each year, while experienced data scientists working in already high-paying industries such as investment banking and management consulting can make six figure salaries.
Data Science: Industry Outlook
Simply put, the outlook for data science is strong. The industry is fast-growing and shows no signs of stopping. Three trends within the data science job market are of particular interest to recent graduates.
- Junior Applicants. Many more students have caught on to the big data craze, realizing that they do not need an advanced degree to land a job. In recent years the number of bachelor’s degree students applying for entry level data science positions has risen significantly. Fortunately, the robust growth the data science industry has so far been able to keep up with demand. And competition is not as fierce as it could be.
- Master’s Degrees > PhDs. Those students who have opted for advanced degrees are looking to make even faster transitions into the job market. As data science has gone well beyond an academic subject to become a force in the private sector, students who a few years ago might have committed to 5+ year PhD programs are now opting for 1-2 year master’s degrees, allowing them to rapidly transition from school to lucrative mid-level positions.
- Established Professionals. Many experienced professionals from related fields involving forecasting, modeling, and predictive analysis have begun making lateral moves into data science. These professionals are leveraging industry connections and years of business operations experience to quickly integrate themselves into the data science industry. Those with prior programming experience have taken crash courses in data science with services like Coursera and General Assembly. While professionals with leadership experience have sought out teams of aspiring data scientists to complement their own predictive modeling expertise.
Should you apply for entry level positions with a bachelor’s degree or go for a master’s degree before entering the job market? It’s not an easy choice; and there’s no one right answer.
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