OVERVIEW

Data Scientists emerge from various backgrounds. Some hail from research fields like Biology, Chemistry and Physics while others rise from more quantitative backgrounds like Mathematics, Statistics and Economics, then there are those with Computer Science backgrounds, engineers… developers… journalists all stirring into the Data Scientist community. Some train themselves through experience, while others learn through academia. What they all have in common are two basic skills: a penchant for mathematics and above average analytical and critical thinking skills. During a great talk at ODSC Europe 2016 Neil Lawrence, Senior Principal Scientist at Amazon said, “There’s a sea of data, but most of it is undrinkable. So… who is a part of this desalination process?” This report expands on this metaphor in order to breakdown the field for those new to Data Science. Some may assumed that Data Scientists are to manage the entire wave of Big Data on their own, but this is not the case… growing are the careers, tools, and skills in the realm of data science. Companies do vaguely acknowledge this growth, and know that hiring in new capability will depend on their company’s needs, but understanding who to hire and why remains hugely preventative. Because some comp anies don’t know what they need, and because the separation between Data Scientist, Data Engineer and Data Analyst is not yet clear in the industry you can imagine the frustration of hiring managers and CxOs drowning below the learning curve. In this case we’d recommend connecting with one of our Data Science Associates for some direction or read on and start to understand some Data Science roles. On this wave of data scientist skills, it may be in a company’s interest to unbundle the processes of data wrangling and integration, from mathematical and statistical modeling. Experienced data modelers are harder to find and companies value their time more. As far as those who integrate data, developers can learn and master this skill faster than gaining the experience of mathematical modeling. As a means to avoid hiring a one or two person with ALL the necessary skills, a data scientist, you could contract pieces of this work out, or incentivize your employees to gain new skills in order to leverage existing structured data and data science to inform your business. Professional development of existing staff can be the first pivot toward becoming more data driven, while sidestepping some of the managerial hurdles intrinsic to organizational change. Be advised that these bisected technical capabilities only deserve a further title distinction when they shift into the following categories.

THE DATA ENGINEER

Often, Data Engineers are good at optimizing the flow of existing computing technologies, the deployment and maintenance of big data platforms, and processing big data volumes; data engineers care for the architecture and infrastructure. Their concerns exist at the base of the data wave, where formation is more regular and expected, where efficiency and performance can be optimized, and where data architecture and analysis pipelines lie. Data Engineers try to automate their work. Pretty much everything from – data integration, deploying new models created by data scientists, connecting new pieces of software to the architecture, to maintaining the installed platforms – can be scheduled. Automation increases Data Engineers’ utility of time, which is why they can provide support to a number of Data Scientists each. Some companies report 1 Data Engineer per 5 to 10 Data Scientists, but it varies.

THE BI DEVELOPER

A BI, Business Intelligence, developer usually works with the enterprise data warehouse where data is highly structured, well defined… it’s categorized already. The world of data science is an open one, however, BI developers work in a closed environment between operational systems and information systems. Their domains are Relational Databases, ODSs (Operational Data Stores) and DWHs (Data Warehouses.) In Business Intelligence it is preferable to use the same tool across processes. For example, a single vendor will provide all the Database, ETL and visualization tools in one platform for BI problem solving, however in reality a BI Developer may end up with a collection of packaged tools meant to solve specific tasks. In data science, the diversity of programming languages and modeling tools grants competent data scientists the freedom and flexibility to include more robust processes into data pipelines. Business Intelligence teams have gained territory storing and providing information the company commonly uses, the quality they assure in their deliveries allows the Data Scientists to pick up their work and navigate it in deeper waters. BI developers can do well with data integration processes, but that doesn’t mean they’ll be great mathematical modelers. When you hire a Data Scientist, be sure they know the fundamentals, it is important they know how to get results using a mathematical tool, and how to tune it or adjust it in order to best solve the problem at hand. Basically, they need to know the basics. “A fool with a tool, is still a fool” – Grady Booch, software designer

THE DATA ANALYST

The Data Analysts may be scattered in various departments throughout a company, they provide data and information to the chiefs and managers of the department. They translate needed information into reports. They have advanced skills working with spreadsheets and often gain SQL knowledge along the way to query data for their analysis. Data Analysts mostly work with structured data. They are heavy users of the Data Marts and Data Warehouses. Data Analysts are not used to processing vast amounts of data and sometimes they struggle when the volume of data increases. In terms of professional development, they are likely to grow into more skillful data science and big data engineer positions, for which they are uniquely qualified given their depth of company insight.

THE DATA SCIENTIST

Those who operate in environments requiring seasoned discernment are Data Scientists. Data Scientists identify solutions in the crest of the wave, where problems are specific and messier. They’ll select or design the right algorithms to cull through the noise in order to reveal a solution, then elegantly communicate that solution through data visualizations and storytelling. Data Science is about making Data Products. Data Products are tangible artifacts updated occasionally and systematically throughout their lifecycle. Data Scientists have the ability to build a data product to solve unique problems. The data product can be a machine learning model, an indicator, a score, an application, an API, you name it. The value of a Data Scientist’s critical thinking process is that they can identify a business’ necessity, and spot the right question to solve. A Data Scientist understands a (business) problem, finds the right data (after surfing in tons of datasets), finds the right approach to the problem (after several trials), and creates, adjusts and deploys a data product. Summing and exploring new datasets, or creatively pulling new data out of thin air is all part of an expert Dsts process. The expected skills of Data Scientist: Mathematical modeling, Coding (in Python, R, Scala, Java, etc.), Relational Databases and SQL, Data Integration, Data Processing, NoSQL and unstructured data, Source Version Control, Platforms and frameworks, Analytical thinking, Critical thinking, Scientific method, Variable selection, Dimensionality reduction, Machine Learning, Statistics, Deep Learning, NLP (Natural Language Processing), Social Network Analysis, Creativity, Curiosity, Data Visualization, Storytelling, Data Interpretation, Teamwork, Ethical practices, Business alignment, Strategic, Agile project management. It is difficult to find someone with all the required or expected skills, that is why we call them Narwhals, (the real unicorns of the data ocean). Though the DS Narwhal exists, they are scarce, so companies tend to separate the roles, that is how the former roles entered the picture. There is high demand for these Narwhal solutions, but they may not be the answer to your need. Many companies just lack the fundamental infrastructure and workflows. This reality motivates more and more academics to migrate from their labs to the business world, learning fast the ways of business and proving their value. New university programs and workshops on Data Science as applied to business keep cropping up to fill in the gap of knowledge expected of new Data Scientists. With new applications and methodologies spurring innovation across the industry it’s reasonable to assume that the stream of data inclined will continue flooding workshops and programs all over the globe. Davenport and Patil’s article posited that Data Scientists would be inquisitive visionaries translating data and information to insight without writing code, and the profession certainly draws the type, but for now the basics remain the basics. The combination of hands on technical and business trainings geared toward developing an adept industry acumen remain critical.

 

WARNING: You may be contracting a company touting Data Science capability that hires “Data Scientists” for marketing purposes. Before you deal with that company, scrutinize their data science klout and make sure they have the capability their branding promises.