These Data Science Profiles can boost your career

With the exponential rise in data generation, most organizations are turning towards data science professionals to analyze and interpret the data, helping in predicting future scenarios. Data scientists are responsible for processing the data and using algorithms to collect, store and optimize it. According to the U.S. The Bureau of Labor Statistics reports the demand for data science skills will result in a 27.9% rise in employment opportunities by 2026. Here are the 10 high-paying data science profiles to choose from in 2021.

1. Data Scientists

Data Scientists has been named the number one among the data science careers opportunities in the U.S. by Glassdoor. It is a more technical position with roles to design the methods to store, gather and analyze large structured and unstructured data to help companies improve their strategic decisions.  The average salary of a data scientist is nearly $139,840.

2. Machine Learning Engineers

Machine learning engineers are responsible for creating algorithms needed for analyzing data. These engineers use many big data tools and programming frameworks to modify the raw data being captured by the pipelines. The role requires skills such as knowledge of software engineering, data science, deep learning, and artificial intelligence. Machine learning engineers are top among the data science profiles of 2021. The average salary of a machine learning engineer is about $114,826.

3. Machine Learning Scientist

The main role of a machine learning scientist is to research new data approaches and algorithms that are used in adaptive systems including unsupervised, supervised, and deep learning techniques. Machine learning scientists are also referred to as research scientists or research engineers which is one of the data science profiles to choose for 2021.  The average salary that a machine learning scientist draws is nearly $113,757.

4. Applications Architect

An application architect builds various applications for companies that almost every organization needs to run user interfaces efficiently.  They are also responsible for tracking the functions of various applications. This is one of the data science profiles which can earn you up to $113,757.

5. Enterprise Architect

An enterprise architect is responsible for aligning an organization’s strategy with the new technologies needed to carry out the objectives. This is one of the data science profiles that demands to have a complete understanding of the business and the technologies to design the systems architecture required to meet the needs of the organization. The average salary of an enterprise architect is nearly $110,663.

6. Data Architect

Data architects are the main masterminds behind designing the database framework of the company. They are responsible for designing, creating, and launching a company’s overall database architecture depending on the business requirements of the organization. This among the data science profiles requires also maintaining the data ecosystem of the company. The average salary of a data architect is up to $108,278.

7. Infrastructure Architect

Generally, the infrastructure architect checks for various databases, applications, and software for effective functioning purposes. They are responsible for making sure that the company has all the essential tools for analyzing big data. Infrastructure architect also makes to the list of data science profiles of 2021 with an average salary of $107,309.

8. Data Engineers

Data engineers maintain and create database architectures and frameworks. They are responsible for understanding what the business wants to achieve from the datasets and then developing algorithms according to that.  Data engineers also create dashboards, reports, and other data visualizations for different companies and stakeholders. The average salary of a data engineer is about $81,514 making it one of the data sciences profiles of 2021.

9. Business Intelligence Developer

Business Intelligence developers or analysts create efficient business models and strategic plans for the businesses. They define key performance indicators and data warehouses strategies. They also use advanced software tools to identify and mine business intelligence enabling the organization to make better-informed decisions. This is one of the data science profiles to choose with an average salary of about $81,514.

10. Data Analyst

As every industry relies on data, the role of data analysts comes into play to convert large data sets into a format that is suitable to analyze the data. They also have a crucial part in the industry of data science making it one of the data sciences profiles of 2021 to choose. They also ensure that all programs and systems are effectively running at an optimal level. The average salary of a data analyst is nearly $62,453.

Take away

Since data scientists are needed in almost all fields ranging from government security to dating apps, these industries run on huge amounts of data. Data science careers are in huge demand and this is not going to slow down any soon so choosing from these top 10 data science profiles can boost your career to a great extent.

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Let’s Check on The Top 10 Highest Paying Jobs in Technology for 2021.

With the evolution of modern technology and wholesome digital transformation, there is enormous availability of the highest-paid jobs in technology. In the past career options were limited, there were only a few jobs in specific industries like medicine or IT. But now there are ample opportunities where one person can get certified even when they are still working.

Want to give your career a good start and to make sure about your skillset?

Here is the list of the top 10 highest-paid tech jobs for you.

1. Data Scientist

For any organization today, data is the most valuable asset. Therefore, the key responsibility of a data scientist stands to analyse and identify all data that are collected and then interpret the complex variables and forms of those data. For becoming a data scientist, one needs to have enough knowledge about machine learning, data visualization, deep learning, mathematics, computer science.

Job role for data scientists-

• Constructing data models
• Coding languages like Python and other analytical tools
• Understanding and working with machine learning algorithms
• Detecting business problems and providing solutions
• Data scientists can earn as much as $150,000 per annum

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2. Big Data Architect

Users generate over 2.5 quintillion bytes of data daily, but this data in its raw form is impractical and useless. Therefore, over 98% of organizations are investing in Big Data to identify and analyze these data and interpret them. Here stands the need for Big Data architects. Job role for Big Data architect- planning, designing, and managing large-scale deployments of Big Data applications from beginning to end. Skills required for this role are programming skills, data visualization skills, communication skills.

The average salary for a Big Data architect is $140,000 per annum.

3. IoT (Internet of Things) Solutions Architect

One of the fastest-growing technology today is the Internet of Things (IoT). It caters to swift and flawless data sharing by using AI and machine learning. The IoT solutions architect connects the gap between technical and non-technical roles. An IoT solutions architect is responsible for leading and participating in all activities related to architecture and design. An IoT architect should have skills such as hardware design and architecture, strong programming skills, knowledge of Machine Learning.

IoT Architects can earn an average of $130,000 per annum.

4. Software Architect

The main job of a software architect is optimizing the development process by making choices related to design and creating technical standards of coding, tools, and platforms. They are hired by the organization for testing and developing software for them. The job role for software architects is understanding customer demands and perform hands-on work to develop software prototypes. Mandatory skills to become a software architect are understanding software architecture, programming skills, strong analytical skills, and data modeling.

The demand for software architects is very high, with organizations paying well over $114,000 per annum.

5. Blockchain Engineer

This technology constitutes links of chains of information and data that keep getting added to a particular network. A blockchain engineer with the help of blockchain technology develops and implements architecture and solutions. Thorough understanding of Ripple, R3, Ethereum, Bitcoin, etc; understanding of consensus methodologies and the security protocol stacks, crypto libraries, and functions; strong programming skills are required for a blockchain engineer.

As a blockchain engineer, one can earn an average of $150,000 a year.

6. Artificial Intelligence (AI) Architect

The task of an AI architect is to define, identify, design, and build solutions that cater to growth and productivity for organizations through automation. To become an AI architect, skills such as deep knowledge of mathematics and statistics, Solid programming skills with knowledge of Python, R, and Torch, working knowledge of TensorFlow and similar technologies, understanding of technologies related to AI like Machine Learning, neural networks, and Deep Learning are required.

An average salary of an AI architect is $110,000 per annum.

7. Cloud Architect

Cloud computing is a mainstream technology that organizations and individuals across the world use on a daily basis. This resulted in openings for many new jobs as a cloud architect. The task of a cloud architect is to help an organization to develop cloud architecture, cloud strategy, coordinating its execution and application. To become a cloud architect, one needs to have a deep understanding of cloud application architecture, knowledge of Amazon Web Services (AWS), Microsoft Azure, or Google cloud platform, excellent communication skills.

A cloud architect can earn $107,000 per annum on an average in this field.

8. Full-Stack Developer

The task of a full-stack developer is to control and develop both the front-end and back-end of an organization. A full-stack developer must have knowledge about technologies like MongoDB and Node.js, coding and scripting, basics of database technology, fundamentals of web development and should know how to design and develop an API.

The average income of a full-stack developer is $106,000 per annum.

9. DevOps Engineer

The job of a DevOps engineer is processing tools and methodologies that are required to stabilize the needs through the life cycle of the software development process. A DevOps engineer designs and maintains a deployment structure, applies cloud services to allow mechanization in Python and Ruby. To become a DevOps engineer, a person must know coding and scripting, deployment and network operations, to use tools like Git and Jenkins, Using Linux or Unix system administration.

Their average annual salary ranges from $95,000 to $140,000.

10. Product Manager

A product manager is responsible to help the organization in identifying parameters for a product that is built by the engineering team. A product manager is required to lead the development process for a product from its conception to its launch. Product managers also identify the vision of a product and work thoroughly with other teams like sales, marketing, engineering, and IT. A product manager must have a thorough understanding of the concept of PLM, should have the knowledge to manage product tools PivotalTracker, JIRA, or Asana, must have analytical skills and strong time management skills to meet the deadline.

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The hype for highest paying companies for Data Scientists attracts more Aspirants

The data science landscape is filled with opportunities spanning diverse industries. As new technologies are being added to the digital sphere year-on-year, the transformation is likely to continue into the coming decade. Owing to the increasing influence of technology in our daily lives, the demand for data science jobs has drastically spiked. The openings for data scientists are expected to go beyond 2021, adding more than 150,000 jobs in the coming years. This trend is a natural response of the digital age for adding more data into its ecosystem. Besides paying high salaries, data science jobs are demanding when it comes to talent requirements and innovation. Data science requires the expertise of professionals, who possess the skill of collecting, structuring, storing, handling and analyzing data, allowing individuals and organizations to make decisions based on insights generated from the data. On a positive note, the nature of data science jobs allows an individual to take on flexible remote works and also to be self-employed. Despite the leniency, the hype for highest paying companies for data scientists remains at the top. In this article, Analytics Insight has listed the top 10 companies that are paying a fortune for data scientists in 2021.

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Top companies paying high salaries to data scientists

Oracle

Data Scientist’s salary: US$124,333

Oracle is one of the largest vendors in the enterprise IT market and the shorthand name of its flagship product, a relational database management system that’s formally called Oracle Database. In 1979, Oracle became the first company to commercialize an RDBMS platform. The enterprise software company offers a range of cloud-based applications and platforms as well as hardware and services to help companies improve their processes. Oracle recently announced the availability of its cloud data science platform, a native service on Oracle Cloud Infrastructure (OCI).

Pinterest

Data Scientist’s salary: US$162,931

Pinterest is a social sharing website where individuals and businesses can ‘pin’ images on ‘boards’ in order to share visual content with friends and followers. Today, many businesses are using interest as a source to enhance their business by promoting content in it. Pinterest creates a lot of online referral traffic so it’s great for attracting attention. Pinterest has a special data science lab where its leading data scientists work to accelerate the company’s development. So far, the data science team has created a systematic approach to data science, which gives them trustworthy conclusions that are both reproducible and automatable.

Lyft

Data Scientist’s salary: US$157,798

Lyft is an online ridesharing provider that offers ride booking, payment processing, and car transport services to customers in the United States. Introduced in 2012, Lyft leverages a friendly, safe, and affordable transportation option that fills empty seats in passenger vehicles already on the road by matching drivers and riders via a smartphone application. Owing to its need for data science professionals, Lyft has so far assembled a team of over 200+ data scientists with a variety of backgrounds, interests, and expertise.

Uber

Data Scientist’s salary: US$146,032

Uber is also a transportation company, well-known for its ride-hailing taxi app. The company has since become synonymous with disruptive technology, with the taxi app has swept the world, transforming transportation and giving a different business model, dubbed uberisation. Founded in 2009, the app automatically figures out the navigational route for drivers, calculates the distance and fare, and transfers the payment to the driver from users’ selected payment method. Therefore, data science is an internal part of Uber’s products and philosophy.

Walmart

Data Scientist’s salary: US$137,668

Walmart is one of the biggest retailers in the world started by Sam Walton. The company sells groceries and general merchandise, operating some 5,400 stores in the US, including about 4,800 Walmart stores and 600 Sam’s Club membership-only warehouses. Through continuous innovation and the implication of technology, the company has created a seamless experience to let its customers shop anytime and anywhere online and offline. Walmart has a broad big data ecosystem that attracts more data scientists into the entity.

Nvidia

Data Scientist’s salary: US$197,500

Nvidia is an artificial intelligence computing company that operates through two segments namely graphics and compute & networking. Nvidia is known as a market leader in the design of graphics processing units, or GPUs, for the gaming market, as well as systems on chips, or SOCs, for the mobile computing and automotive markets. Nvidia works on the motive that accelerated data science can dramatically boost the performance of end-to-end analytics workflows, spending up value generation while reducing cost.

Airbnb

Data Scientist’s salary: US$197,800

Airbnb takes a unique approach towards lodging by providing a shared economy. The platform offers someone’s home as a place to stay instead of a hotel. Airbnb began in 2008 when two designers who had space to share hosted three travelers looking for a place to stay. Today, millions of hosts and travelers choose to create an Airbnb account so they can list their space for rentals. The company is using data science to build new product offerings, improve its services, and capitalize on new marketing initiatives.

Netflix

Data Scientist’s salary: US$173,503

Netflix is a streaming entertainment service company, which provides subscription services streaming movies and television episodes over the internet and sending DVDs by mail. For millions, Netflix is a de facto place to go for movies and series. Netflix was founded in 1997 by two serial entrepreneurs, Marc Randolph and Reed Hastings. Data science plays an important role in the Netflix routine. With the help of data science, the company gets a more realistic picture of its customers’ taste in form of graphs and charts. It eventually helps the platform’s recommendation service.

Dropbox

Data Scientist’s salary: US$145,172

Dropbox is a cloud storage service company that lets users save files online and sync them to their devices. Dropbox is one of the oldest and most popular cloud storage services that has strongly outperformed Microsoft’s OneDrive and Google Drive. Founded in 2007, the company offers a browser service, toolbars, and apps to upload, share, and sync files to the cloud that can be accessed across several devices.

Genentech

Data Scientist’s salary: US$129,833

Genentech is a biotechnology company that discovers, develops, manufactures, and commercializes medicines to treat patients. The company offers medicine for the prevention of oncology, immunology, metabolism, monoclonal antibodies, small molecules, tissue repair, and virology, as well as conducts scientific research to produce biologic medicines. The company uses its data science capabilities to enhance its performance in the market by unraveling effective medicines.

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If you’re looking for a data science role in financial services, and are expecting to earn an enormous salary and bonus, you may well be disappointed. Data science roles in financial services come in a variety of forms and some pay a lot better than others. 

A new salary and bonus survey from recruitment firm Octavius Finance highlights the discrepancies. Not only are some roles more lucrative, but some types of employer are more generous too.

Octavius Finance splits data science jobs into five categories, defined as follows:

• Data strategists and relationship managers: People who researching new data sources and manage vendor relationships.
• Data analysts: People who engage in exploratory data analysis to make data consumable.
• Data engineers: People who clean and organize data and build data pipelines
• Data scientists: People who harvest Alpha, combine alpha signals, engage in systematic backtesting and machine learning.  
• Quant researchers: People who building systematic trading models, strategies and trading signals

As the pay charts below show, data scientists and quant researchers are by far the best paid in London. Moreover, hedge funds are far more lucrative places to work as a data scientist than investment banks. 

Quant researcher salaries and bonuses are shown in the final chart on their own. Octavius director Natalie Basiratpour says hedge fund bonuses aren’t included because they vary widely depending upon factors including the size of the team, assets under management and the performance of the signal. 

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