How to Become a Data Science Engineer

Data Science

A Data Science Engineer is someone who builds AI tools to automate processes that make value out of data. They play a valuable role in modern businesses that have to deal with masses of unstructured, semi-structured, or structured data. The current demand for this service has made such professionals highly sought-after. However, becoming a data science engineer is not as easy as it may seem. This is where data science certification can help you become a data science engineer.

To understand how to become a data science engineer, you’ll need a bit of insight into who they are and why employers need them.

Who is a Data Science Engineer?

A data science engineer is essentially someone who provides a reliable infrastructure for data logging, flow, cleaning, and other operations. They are involved in the first 2-3 stages of the AI Hierarchy of Needs: collect, move and store, and data preparation.

Data science engineers are derived from specialists in software engineering and backend development. That’s because employers need them to write large SQL queries, and handle data using tools like Informatica ETL, Talend, and Pentaho ETL.

That’s not all.

Companies often require these professionals to have an advanced capacity in SQL, Java / Scala, Python, and expert use of cloud platforms (especially Amazon Web Services). Companies that generate massive data from multiple sources need the data science engineer to organize the collection, processing, and storage of information. Hence, those tools are valuable in data modeling, data warehousing, and related tasks.

Roles and Responsibilities of a Data Science Engineer

To appreciate the role of a data science engineer, you need to differentiate it from that of a data scientist.

While a data engineer is tasked with the job of developing, constructing, testing, and maintaining such architecture as large-scale processing systems and databases; a data scientist does cleaning, massaging, and organizing of data.

Data engineers recommend and implement ways of improving data reliability, quality, and efficiency.

They achieve their role using a variety of computer languages and tools, which interlink different systems and pull new data from other systems.

Some of the typical responsibilities you’ll have in this profession include:

  • Using machine learning techniques to select features, and build and optimize classifiers
  • Deploying state-of-the-art methods to data mine
  • Incorporating third-party information sources to extend company data
  • Building analytic systems by including information which enhances data collection procedures
  • Cleansing, processing, and verifying the integrity of data used in the analysis
  • Constantly tracking performance with automated anomaly detection systems

Trends Affecting Data Science Engineers

Jobs for data science engineers are expected to grow by 28 percent, according to research done by IBM.

More and more businesses are relying on hard information to make decisions. That means an increasing need for professionals to organize, store, and interpret the information.

Many companies are already hiring data science engineers, including:

  • Cognizant Technology Solutions (CTS)
  • Tata Consultancy Services (TCS)
  • Capgemini
  • Wipro Technologies
  • Hewlett-Packard (HP)
  • HCL Technologies
  • Mahindra Satyam
  • LatentView Analytics
  • Mu Sigma
  • Deloitte
  • Wells Fargo
  • PayPal
  • JPMorgan Chase
  • Dell
  • Fractal Analytics
  • Opera Solutions
  • Nielsen
  • Equifax
  • Cisco Systems
  • Infosys
  • KPMG
  • Adobe
  • Target
  • American Express
  • CSC
  • CIBC
  • HSBC
  • Revolution Analytics
  • MarketShare
  • Walmart Labs
  • UK Based RPO
  • Groupon
  • Verizon
  • T-Mobile

Certain locations are also known to offer the highest salaries for such professionals. The top locations include:

  1. Houston – The Woodlands-Sugar Land, TX — $137,648 average salary
  2. San Francisco – Oakland – Hayward, CA — $166,519 average salary
  3. Seattle – Tacoma – Bellevue, WA — $146,088 average salary
  4. Atlanta – Sandy Springs – Roswell, GA — $117,002 average salary
  5. San Jose – Sunnyvale – Santa Clara, CA — $153,535 average salary
  6. Bridgeport – Stamford – Norwalk, CT — $144,444 average salary
  7. New York, Newark, Jersey City, NY – NJ – PA — $146,067 average salary
  8. Boston – Cambridge – Newton, MA – NH — $132,922 average salary
  9. Austin – Round Rock, TX — $119,359 average salary
  10. Chicago, Naperville, Elgin, IL – IN – WI — $123,713 average salary

Who can Become a Data Science Engineer?

To become a data science engineer, you need particular critical skills beyond the educational qualifications. These skills are essential in executing your duties and producing excellent performance.

Here’s an overview of several critical skills:

  • A curious nature – you need a constant pursuit of learning. Since you’ll be dealing with so many areas and data points, you need an inherent curiosity driving your need to find answers.
  • Excellent organization – without excellent organization skills, you’ll be overwhelmed by the millions of potential data points you have to manage. Good organization is necessary if you want to reach the right conclusions.
  • Persistence/ stubbornness – this profession can be filled with numerous frustrations. Trying to find answers to challenging problems may seem impossible, and anyone without much determination will give up easily.
  • Creativity, focus, and attention to detail – the combination of these traits ensure that you’re always trying new things, delivering results, and never miss out on any valuable aspect regardless of how unimportant it may seem.
  • Exceptional communication skills – you’ll be collaborating with other team members to fulfill major projects. The success of those projects depends on how well you communicate.

Apart from those soft skills, you need hard skills that you’ll use daily at work.

Here are some of the hard skills you need:

  • Superb understanding of machine learning techniques and algorithms, like Naive Bayes, k-NN, SVM, and Decision Forests, among others.
  • Experience handling common data science toolkits, like Weka, R, NumPy, and MatLab, among others. The specific toolkit needed depends on the project you’re handling. You should excel in at least one.
  • Experience managing data visualization tools like GGplot and D3.js.
  • Proven ability to use query languages like Hive, SQL, and Pig.
  • Experience using NoSQL databases like Cassandra, MongoDB, and HBase.
  • Decent applied statistics skills including regression, distributions, and statistical testing.
  • Excellent programming and scripting skills.

Generally, you must have a data-oriented personality to succeed in this profession.

Conclusion

Having all this information regarding the trends in data science, prospects of employment, companies hiring, and the required skills, you’re one step closer to launching your career as a data science engineer.

The next step is getting the necessary educational qualification, which can be achieved through a bachelor’s and a master’s degree in data or related fields.

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Featured Image Source: Dataquest

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