So, you want to be a data scientist? It’s a good choice: combining statistics, computer science and business acumen, this is an exciting and fast-growing career path, and while the term “data scientist” was first coined in 2008, the career itself has been around for much longer.
The job market for data scientists is growing quickly: more and more companies are not only collecting electronic data on customers’ shopping habits or health records, for example, but they also want to use this data to guide them and make decisions.
Employment of data scientists is projected to grow 36% by 2031, which is considerably faster than the average for all occupations. About 13,500 openings for data scientists are projected each year, on average, over the decade, says the U.S. Bureau of Labor Statistics.
What do data scientists do?
As a data scientist, you’re responsible for gathering, storing and analyzing large sets of data using statistical tools to find patterns or trends that can be used for business or educational purposes.
Data science is a skill set, but it's also a field of study and a job title. Data scientists are the people who use that skill set to answer questions by analyzing data. They're also often called on to make predictions or help others make informed decisions based on their analyses of data.
Data scientists don't just pull numbers from data sources. They explore relationships within huge sets of information—often millions or billions of inputs—and find insights that would be difficult to see with other techniques.
To do this efficiently, they need advanced statistical skills along with programming expertise in languages like Python or R (or both). Data scientists may also have deep knowledge in areas like artificial intelligence (AI) or natural language processing (NLP), depending on the type of problem they're working on.
What are the main types of data analytics?
There are four main types of data analytics. Descriptive analytics is the simplest type of analytics and is where a data scientist will pull raw data from a source and examine it to discover what has happened.
Diagnostic analytics is the next step: here you’ll take a look at data to assess patterns – for example seasonal sales surges – to discover why something is happening. Predictive analytics, then, is useful for helping you make informed predictions about what the future could hold, and prescriptive analytics is involved with working out what to do next.
How can you become a data scientist?
While some people learn on the job – and this is the kind of career where you’ll never stop learning – others use online tutorials or course resources.
The best route to becoming a data scientist is to get a graduate degree in statistics or mathematics. A good data science program will also teach you programming skills, experience with data analysis tools and communication skills, and creative problem-solving skills.
Data visualization is also important for communicating results, and a good data scientist should have some knowledge of databases so that they can clean the data and extract useful information from it.
What sort of jobs are available?
Data science is growing quickly. According to Indeed, the average salary for data scientists is $144,451 and there is plenty of scope across in terms of job titles, too.
These are just some of the roles you can consider:
- Data Analyst
- Data Engineers
- Database Administrator
- Machine Learning Engineer
- Data Scientist
- Data Architect
- Business Analyst
- Data and Analytics Manager
Data scientists are valuable across all facets of a business. Anyone who makes decisions needs data to do so, and so you’ll typically find data science jobs in marketing and product departments, across the fields of finance and banking, and in human resources and recruitment, for example.
Where can I find a great data science job?
The Hackr.io Job Board is your first port of call to discover companies all across the U.S. which are hiring data scientists. Below, we’re looking at three top tech companies which are hiring data scientists right now.
Data Science Manager, PayPal, San Jose
PayPal seeks an exceptional Data Science Manager, who will be a world-class problem solver with a passion for data insights and a relentless execution focus.
You’ll be expected to have strong SQL and visualization skills with a lot of attention to granular details in data, as well as expertise in stitching together findings to convey coherent insights.
You'll have the ability to perform deep-dive analysis of key business trends from multiple perspectives and package the insights into easily consumable presentations and documents.
You will need at least seven years of experience, a data-driven mindset with a Bachelor’s/Master’s degree in any quantitative discipline and you’ll be fluent in SQL, Excel, and visualization tools such as Tableau or Qlikview; experience with a statistical programming language like R or Python preferred.
Sr. Data Science Manager, Core Data Science - Build (Remote), Shopify, Boston
Shopify is looking for a strong Senior Data Science Manager to define and guide data strategy, resulting in data-powered solutions that scale.
You will provide technical leadership to data science teams, and build data pipelines using Python, Spark, SQL, Mode, and Tableau. Cultivating a data-informed culture and delivering meaningful insights that drive product roadmaps and optimize business strategies are key.
To apply, you will have been a leader in multiple tech companies where analytics on top of massive data sets drives product and business decisions, and you have a demonstrated track record of attracting top data/analytics talent, developing high-performance teams, and a knack for keeping top talent happy, challenged, and impactful.
Get the full job description here.
Data Scientist, Info Apps, Apple, Culver City
Apple’s app and services engineering team is looking for an outstanding Data Scientist who will focus on applied research, modeling pipeline, and development of innovative components to enhance user experience.
You will face a variety of problems to solve, and you will be equipped with strong analytical and quantitative skills. Your creativity and critical thinking skills will be put to good use, deconstructing problems and transforming your insights into data-backed recommendations.
You will need superb coding skills and hands-on experience with big data, you’ll be proficient in writing code and you will have Hadoop experience (MapReduce, Hive, Spark SQL), and SQL. Plus, you will possess strong product intuition, data analysis skills, as well as business presentation skills.