{"id":877,"date":"2018-09-07T02:49:24","date_gmt":"2018-09-07T02:49:24","guid":{"rendered":"http:\/\/kusuaks7\/?p=482"},"modified":"2023-07-26T13:24:08","modified_gmt":"2023-07-26T13:24:08","slug":"top-ten-roles-for-your-data-science-team","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/top-ten-roles-for-your-data-science-team\/","title":{"rendered":"Top Ten roles in AI and data science"},"content":{"rendered":"

Ready to learn Data Science? Browse\u00a0Data Science Training and Certification<\/a> courses developed by industry thought leaders and Experfy in Harvard Innovation Lab.<\/em><\/strong><\/p>\n

When you think of the perfect data science team, are you imagining 10 copies of the same professor of computer science and statistics, hands delicately stained with whiteboard marker? I hope not!<\/p>\n

Applied data science is a team sport that\u2019s highly interdisciplinary. Diversity of perspective matters! In fact, perspective and attitude matter at least as much as education and experience.<\/p>\n

If you\u2019re keen to make your data useful with a decision intelligence engineering approach, here\u2019s my take on the order in which to grow your team.<\/p>\n

#0 Data\u00a0Engineer<\/strong><\/h3>\n

We start counting at zero, of course, since you need to have the ability to\u00a0get<\/em>data before it makes sense to talk about data analysis. If you\u2019re dealing with small datasets, data engineering is essentially entering some numbers into a spreadsheet. When you operate at a more impressive scale, data engineering becomes a sophisticated discipline in its own right. Someone on your team will need to take responsibility for dealing with the tricky engineering aspects of delivering data that the rest of your staff can work with.<\/p>\n

#1 Decision-Maker<\/strong><\/h3>\n

Before hiring that PhD-trained data scientist, make sure you have a decision-maker who understands the art and science of data-driven decision-making.<\/p>\n

Decision-making skills have to be in place before a team can get value out of\u00a0data.<\/p><\/blockquote>\n

This individual is responsible for identifying decisions worth making with data, framing them (everything from designing metrics to calling the shots on statistical assumptions), and determining the required level of analytical rigor based on potential impact on the business. Look for a deep thinker who doesn\u2019t keep saying,\u00a0\u201cOh, whoops, that didn\u2019t even occur to me as I was thinking through this decision.\u201d<\/em>\u00a0They\u2019ve already thought of it. And that. And that too.<\/p>\n

#2 Analyst<\/strong><\/h3>\n

Then the next hire is\u2026 everyone already working with you. Everyone is qualified to look at data and get inspired, the only thing that might be missing is a bit of familiarity with software that\u2019s well-suited for the job. If you\u2019ve ever looked at a digital photograph, you\u2019ve done data visualization and analytics.<\/p>\n

Learning to use tools like R and Python is just an upgrade over MS Paint for data visualization; they\u2019re simply more versatile tools for looking at a wider variety of datasets than just\u00a0red-green-blue pixel matrices<\/a>.<\/p>\n

If you\u2019ve ever looked at a digital photograph, you\u2019ve done data visualization and analytics. It\u2019s the same\u00a0thing.<\/p><\/blockquote>\n

And hey, if all you have the stomach for is looking at the first five rows of data in a spreadsheet, well, that\u2019s still better than nothing. If the entire workforce is empowered to do that, you\u2019ll have a much better finger on the pulse of your business than if no one is looking at any data at all.<\/p>\n

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Nessie 1934: This is data. Make conclusions about it\u00a0wisely<\/p>\n

The important thing to remember is that you shouldn\u2019t come to conclusions beyond your data. That takes specialist training. Just as with the photo above, here\u2019s all you can say about it: \u201cThis is what is in my dataset.<\/em>\u201d Please don\u2019t use it conclude that the\u00a0Loch Ness Monster is real<\/a>.<\/p>\n

#3 Expert\u00a0Analyst<\/strong><\/h3>\n

Enter the lightning-fast version! This person can look at more data faster. The game here is speed, exploration, discovery\u2026 fun! (Another term for analytics is data-mining.) This is not the role concerned with rigor and careful conclusions. Instead, this is the person who helps your team get eyes on as much of your data as possible so that your decision-maker can get a sense of what\u2019s worth pursuing with more care.<\/p>\n

The job here is speed, encountering potential insights as quickly as possible.<\/p><\/blockquote>\n

This may be counterintuitive, but don\u2019t staff this role with your most reliable engineers who write gorgeous, robust code. The job here is speed, encountering potential insights as quickly as possible, and unfortunately those who obsess over code quality may find it too difficult to zoom through the data fast enough to be useful in this role.<\/p>\n

Those who obsess over code quality may find it difficult to be useful in this\u00a0role.<\/p><\/blockquote>\n

I\u2019ve seen analysts on engineering-oriented teams bullied because their peers don\u2019t realize what \u201cgreat code\u201d means for descriptive analytics. Great is \u201cfast and humble\u201d here. If fast-but-sloppy coders don\u2019t get much love, they\u2019ll leave your company and you\u2019ll wonder why you don\u2019t have a finger on the pulse of your business.<\/p>\n

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#4 Statistician<\/strong><\/h3>\n

Now that we\u2019ve got all these folks cheerfully exploring data, we\u2019d better have someone around to put a damper on the feeding frenzy. It\u2019s safe to look at that \u201cphoto\u201d of\u00a0Nessie<\/a>\u00a0as long as you have the discipline to keep yourself from learning more than what\u2019s actually there\u2026 but do you? While people are pretty good at thinking reasonably about photos, other data types seem to send common sense out the window. It might be a good idea to have someone around who can prevent the team from making unwarranted conclusions.<\/p>\n

Inspiration is cheap, but rigor is expensive.<\/p><\/blockquote>\n

Lifehack:<\/strong>\u00a0don\u2019t make conclusions and you won\u2019t need to worry.<\/em>\u00a0I\u2019m only half-joking. Inspiration is cheap, but rigor is expensive. Pay up or content yourself with\u00a0mere inspiration<\/a>.<\/p>\n

Statisticians help decision-makers come to conclusions safely beyond the\u00a0data.<\/p><\/blockquote>\n

For example, if your machine learning system worked in one dataset, all you can safely conclude is that it worked in\u00a0that<\/em>\u00a0dataset. Will it work when it\u2019s running in production? Should you launch it? You need some extra skills to deal with those questions. Statistical skills.<\/p>\n

If we\u2019re want to make serious decisions where we don\u2019t have perfect facts, let\u2019s slow down and take a careful approach.\u00a0Statisticians<\/a>\u00a0help decision-makers come to conclusions safely beyond the data analyzed.<\/p>\n

#5 Applied Machine Learning\u00a0Engineer<\/strong><\/h3>\n

An applied AI \/\u00a0machine learning<\/a>\u00a0engineer\u2019s best attribute is not an understanding of how algorithms work.\u00a0Their job is to use them, not build them.<\/a>\u00a0(That\u2019s what researchers do.) Expertise at wrangling code that gets existing algorithms to accept and churn through your datasets is what you\u2019re looking for.<\/p>\n

Besides quick coding fingers, look for a personality that can cope with failure. You almost never know what you\u2019re doing, even if you think you do. You run the data through a bunch of algorithms as quickly as possible and see if it seems to be working\u2026 with the reasonable expectation that you\u2019ll fail a lot before you succeed. A huge part of the job is dabbling\u00a0blindly, and it takes a certain kind of personality to enjoy that.<\/p>\n

Perfectionists tend to struggle as ML engineers.<\/p><\/blockquote>\n

Because your business problem\u2019s not in a textbook, you can\u2019t know in advance what will work, so you can\u2019t expect to get a perfect result on the first go. That\u2019s okay, just try lots of approaches as quickly as possible and iterate towards a solution.<\/p>\n

Speaking of \u201crunning the data through algorithms\u201d\u2026 what data? The inputs your analysts identified as potentially interesting,\u00a0of\u00a0course. That\u2019s why analysts make sense as an earlier hire.<\/p>\n

Although there\u2019s a lot of tinkering, it\u2019s important for the machine learning engineer to have a deep respect for the part of the process where rigor is vital: assessment. Does the solution actually work on new data? Luckily, you made a wise choice with your previous hire, so all you have to do is pass the baton to the statistician.<\/p>\n

The strongest applied ML engineers have a very good sense of how long it takes to apply various approaches.<\/p>\n

When a potential ML hire can rank options by the time it takes to try them on various kinds of datasets, be impressed.<\/p><\/blockquote>\n

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#6 Data Scientist<\/strong><\/h3>\n

The way I use the word, a data scientist is someone who is a full expert in all of the three preceding roles.\u00a0Not everyone uses my definition<\/a>: you\u2019ll see job applications out there with people calling themselves \u201cdata scientist\u201d when they have only really mastered one of the three,\u00a0so it\u2019s worth checking<\/a>.<\/p>\n

Data scientist are full experts in all of the three previous\u00a0roles.<\/p><\/blockquote>\n

This role is in position #6 because hiring the true three-in-one is an expensive option. If you can hire one within budget, it\u2019s a great idea, but if you\u2019re on a tight budget, consider upskilling and growing your existing single-role specialists.<\/p>\n

#7 Analytics Manager \/ Data Science\u00a0Leader<\/strong><\/h3>\n

The analytics manager is the goose that lays the golden egg: they\u2019re a hybrid between the data scientist and the decision-maker. Their presence on the team acts as a force-multiplier, ensuring that your data science team isn\u2019t off in the weeds instead of adding value to your business.<\/p>\n

The decision-maker + data scientist hybrid is a force-multiplier. Unfortunately, they\u2019re rare and hard to\u00a0hire.<\/p><\/blockquote>\n

This person is kept awake at night by questions like, \u201cHow do we design the right questions? How do we make decisions? How do we best allocate our experts? What\u2019s worth doing? Will the skills and data match the requirements? How do we ensure good input data?<\/em>\u201d<\/p>\n

If you\u2019re lucky enough to hire one of these, hold on to them and never let them go. Learn more about this role\u00a0here<\/a>.<\/p>\n

#8 Qualitative Expert \/ Social Scientist<\/strong><\/h3>\n

Sometimes your decision-maker is a brilliant leader, manager, motivator, influencer, or navigator of organizational politics\u2026 but unskilled in the art and science of decision-making. Decision-making is so much more than a talent. If your decision-maker hasn\u2019t honed their craft, they might do more damage than good.<\/p>\n

Instead of firing an unskilled decision-maker, you can augment them with a qualitative expert.<\/strong><\/p><\/blockquote>\n

Don\u2019t fire an unskilled decision-maker, augment them. You can hire them an upgrade in the form of a helper. The qualitative expert is here to supplement their skills.<\/p>\n

This person typically has a social science and data background \u2014 behavioral economists<\/a>,\u00a0neuroeconomists<\/a>, and\u00a0JDM\u00a0psychologists receive the most specialized training, but self-taught folk can also be good at it. The job is to help the decision maker clarify ideas, examine all the angles, and turn ambiguous intuitions into well-thought-through instructions in language that makes it easy for the rest of the team to execute on.<\/p>\n

We don\u2019t realize how valuable social scientists are. They\u2019re usually better equipped than data scientists to translate the intuitions and intentions of a decision-maker into concrete\u00a0metrics.<\/p><\/blockquote>\n

The qualitative expert doesn\u2019t call any of the shots. Instead, they ensure that the decision-maker has fully grasped the shots available for calling. They\u2019re also a trusted advisor, a brainstorming companion, and a sounding board for a decision-maker. Having them on board is a great way to ensure that the project starts out in the right direction.<\/p>\n

#9 Researcher<\/strong><\/h3>\n

Many hiring managers think their first team member needs to be the ex-professor, but actually you don\u2019t need those PhD folk unless you already know that the industry is not going to supply the algorithms that you need. Most teams won\u2019t know that in advance, so it makes more sense to do things in the right order: before building yourself that\u00a0space pen<\/a>, first check whether a pencil will get the job done. Get started first and if you find that the available off-the-shelf solutions aren\u2019t giving you much love, then you should consider hiring researchers.<\/p>\n

If a researcher is your first hire, you probably won\u2019t have the right environment to make good use of\u00a0them.<\/p><\/blockquote>\n

Don\u2019t bring them in right off the bat. It\u2019s better to wait until your team is developed enough to have figured out that what they need a researcher for. Wait till you\u2019ve exhausted all the available tools before hiring someone to build you expensive new ones<\/p>\n

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Before you invent pens that work in space, check that existing solutions don\u2019t meet your needs\u00a0already.<\/p>\n

#10+ Additional personnel<\/strong><\/h3>\n

Besides the roles we looked at, here are some of my favorite people to welcome to a decision intelligence project:<\/p>\n