Hiring Data Scientists- a definitive guide

Data science has become an essential part of many industries today because of the increasing availability and volume of data. Data science techniques can help companies make better decisions, identify patterns, and gain insights from data that would otherwise be impossible to obtain. Data science techniques such as machine learning, data mining, and predictive analytics can be applied to various areas, such as marketing, healthcare, finance, transportation, and more. By leveraging these techniques, companies can gain a competitive edge, improve operational efficiency, and drive innovation.

Additionally, data science can help companies understand their customers better, by analyzing customer behavior, preferences, and feedback. This can help companies improve their products and services, personalize their marketing efforts, and ultimately increase customer satisfaction.

Overall, data science is a critical skill for organizations looking to thrive in today's data-driven economy, and its importance is only expected to grow in the coming years. This blog is a definitive guide to recruiting data scientists. We also have detailed guides to Hiring AI Engineers, Hiring Machine Learning Engineers and Hiring NLP Engineers that might be helpful in your journey.

What is Data Science?

Data science is a multidisciplinary field that involves the use of statistical and computational methods to extract insights and knowledge from data. It involves the collection, processing, analysis, interpretation, and visualization of large and complex datasets.

Data scientists use a variety of tools and techniques such as machine learning, data mining, statistical analysis, and data visualization to analyze data and extract meaningful insights. These insights can be used to solve complex business problems, improve operational efficiency, and create new opportunities. The data used for analysis can come from various sources such as transactional data, social media data, sensor data, web logs, customer feedback, and many more. This data can be structured or unstructured, and it can be presented in various formats such as text, images, audio, or video.

Data science also involves data preparation and cleaning, which is the process of ensuring that the data is accurate, consistent, and complete. This is a crucial step in data analysis as the quality of the insights derived from the data depends on the quality of the data used.

In summary, data science is a complex and rapidly evolving field that helps organizations make informed decisions by leveraging data and advanced analytics techniques.

What are the applications of data science?

Data science has found its applications in almost every industry:

• Healthcare - Healthcare companies are using data science to build sophisticated medical instruments to detect and cure diseases.
• Gaming - Video and computer games are now being created with the help of data science and that has taken the gaming experience to the next level.
• Image Recognition - Identifying patterns in images and detecting objects in an image is one of the most popular data science applications.
• Recommendation Systems - Netflix and Amazon give movie and product recommendations based on what you like to watch, purchase, or browse on their platforms.
• Logistics - Data Science is used by logistics companies to optimize routes to ensure faster delivery of products and increase operational efficiency.
• Fraud Detection - Banking and financial institutions use data science and related algorithms to detect fraudulent transactions.   
• Internet Search - When it comes to search, Google is often the first name that comes to mind. With Google processing over 20 petabytes of data daily, it's safe to say that data science is a crucial component of Google's success and its reputation as the go-to search engine.In addition there are several other search engines like Yahoo, Duckduckgo, Bing, AOL, Ask, and others that utilize data science algorithms to provide the most relevant results for our search query within seconds. 
• Speech recognition - Data science techniques are at the forefront of speech recognition, as evidenced by the impressive performance of algorithms in our everyday lives. Virtual speech assistants like Google Assistant, Alexa, and Siri rely on voice recognition technology to interpret and assess our words, delivering helpful results in response. Similarly, social media platforms such as Facebook, Instagram, and Twitter utilize image recognition to automatically recognize and tag individuals in photos uploaded by users.
• Targeted Advertising -The entire digital marketing landscape benefits from data science algorithms, from display banners on various websites to digital billboards at airports, enabling the identification of almost anything. This is why digital advertisements have a much higher CTR (Click-Through Rate) than traditional marketing methods. They can be personalized based on a user's previous behavior. This is why you might see ads for Data Science Training Programs while someone else sees an ad for clothing in the same area at the same time.
• Airline Route Planning - The airline industry is benefiting from the predictive capabilities of data science, which enables better predictions of flight delays and more informed decisions about when to land at a destination or make a stop along the way. For instance, when flying from Paris to the United States of America, data science can help determine whether it's best to fly non-stop or make a stopover en route.
• Augmented Reality - Lastly, one of the most intriguing future applications of data science is the relationship between it and virtual reality. Virtual reality headsets utilize computer expertise, algorithms, and data to create the most immersive experience possible. While the popular game Pokemon GO is only a small step in this direction, it allows players to explore and capture Pokemon in virtual locations using data from Ingress, a previous app from the same company. As technology advances, the potential for data science to enhance the virtual reality experience is enormous.

These are just a few examples - almost every corner of the business world has been impacted by data science.

Who are Data Scientists?

Data scientists are professionals who use their technical and analytical skills to extract insights and knowledge from large and complex datasets. They have a mix of skills, including mathematics, computer science, and trend forecasting, and they work in both the business and IT sectors.

On a daily basis, data scientists may perform a variety of tasks, including:

1. Discovering patterns and trends in datasets to gain insights: Data scientists use statistical and computational methods to analyze data and identify patterns and trends that may not be immediately apparent. They then use these insights to make informed decisions and recommendations.
2. Creating forecasting algorithms and data models: Data scientists develop algorithms and models that can predict future trends and patterns based on historical data. This enables businesses to make informed decisions about their products, services, and operations.
3. Improving the quality of data or product offerings by utilizing machine learning techniques: Data scientists use machine learning techniques to analyze data and identify areas where improvements can be made to products or services. For example, they may use machine learning to identify patterns in customer feedback data and use this information to improve the quality of products or services.
4. Distributing suggestions to other teams and top management: Data scientists work closely with other teams within an organization to provide insights and recommendations based on their analyses. They also communicate their findings to top management to inform decision-making at a higher level.
5. Using data tools such as R, SAS, Python, or SQL: Data scientists use a variety of tools and programming languages to analyze data and build models. Some of the most popular tools used in data science include R, SAS, Python, and SQL.
6. Staying up-to-date with the latest innovations in data science: Data science is a rapidly evolving field, and data scientists need to stay up-to-date with the latest tools and techniques to remain competitive. They attend conferences, read research papers, and participate in online forums to keep abreast of the latest developments in the field.

Overall, data scientists play a crucial role in helping organizations make informed decisions based on data-driven insights.

What are the Different Types of Data Scientists?

How do Data Scientists solve business problems?

Data scientists follow a series of steps to solve business problems through data analysis. These steps may include:

1. Determining the problem: Before collecting and analyzing data, data scientists need to understand the problem they are trying to solve. This involves asking the right questions, gaining an understanding of the business context, and defining clear objectives.
2. Selecting variables and data sets: Data scientists need to determine which variables and data sets are relevant to the problem they are trying to solve. They may need to gather data from a variety of sources, including enterprise data, public data, and third-party data.
3. Collecting and processing data: Data scientists need to gather and process the data in a way that ensures its quality, completeness, and accuracy. This involves cleaning and validating the data to remove any errors or inconsistencies.
4. Analyzing the data: Once the data is processed, data scientists can use machine learning algorithms or statistical models to identify patterns and trends in the data. They may also use visualization tools to help them interpret the data and communicate their findings.
5. Interpreting the data: Data scientists need to interpret the results of their analysis to identify opportunities and solutions that can help solve the business problem.
6. Communicating the results: Finally, data scientists need to communicate their findings to the appropriate stakeholders, including top management, product teams, and other business units. This may involve creating reports, presentations, or other forms of communication to help stakeholders understand the insights and make informed decisions.

Overall, data scientists play a critical role in helping organizations solve business problems through data analysis. By following a structured approach to data analysis, data scientists can extract meaningful insights from data and provide valuable recommendations to the business.

What skills do Data Scientists have? What tools and technologies do they use?

Data scientists usually hold a Ph.D. or Master’s degree in statistics, computer science, or engineering. This educational background provides a strong foundation for any aspiring data scientist and also teaches the essential data scientist skills and Big Data skills needed to succeed in the field. 

Apart from this foundation some of the important tools and technologies used by proficient data scientists are:

• Statistical analysis and computing: Data scientists should have strong statistical skills to identify patterns and trends in data. They should also be proficient in statistical programming languages such as R and Python.
• Machine Learning: Data scientists should be familiar with machine learning algorithms and techniques such as regression, clustering, decision trees, and neural networks. Data scientists use machine learning libraries such Scikit-learn, TensorFlow, and Keras to build and deploy machine learning models.
• Deep Learning: Data scientists should have knowledge of deep learning techniques such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), and deep belief networks (DBNs).
• Processing large data sets: Data scientists should be skilled in handling and processing large data sets using distributed computing tools such as Hadoop, Spark, and NoSQL databases.
• Data Visualization: Data scientists should be able to effectively communicate insights and findings from data through data visualization tools such as Tableau, PowerBI, and D3.js.
• Data Wrangling: Data scientists should be proficient in cleaning and preprocessing data, dealing with missing values, and managing data quality. Some of the most prominent data wrangling tools used to clean and transform data are OpenRefine, Trifacta, and Talend.
• Mathematics: Data scientists should have a strong foundation in mathematics, including linear algebra, calculus, and probability theory.
• Programming: Data scientists should have strong programming skills in languages such as Python, R Programming, SQL, and Java.
• Statistics: Data scientists should have a solid understanding of statistical concepts and methods, including hypothesis testing, regression analysis, and experimental design.
• Big Data: Data scientists should have knowledge of Big Data technologies such as Hadoop, Spark, and Hive, as well as cloud-based data storage and processing platforms such as AWS and Azure.
• Natural language processing (NLP) tools: NLP tools such as NLTK, spaCy, and Stanford NLP are used to analyze and process human language data.
• Statistical software: Statistical software such as SAS, SPSS, and MATLAB are used to perform statistical analysis and modeling.
• Version control systems: Version control systems such as Git and SVN are used to track changes made to code and collaborate with other data scientists on projects.

The skill set accompanied by the knowledge of the different toolset and technologies mentioned above should narrow your search in finding relevant candidates for your organization.

Which companies are really well known for Data Science?

Apart from tech giants like Google, Amazon, Meta, Microsoft and IBM some of the most prominent and successful companies who have a strong Data Science teams are:

• MuSigma - With a Unicorn status in the United States, MuSigma is amongst the world’s largest pure-play Big Data Analytics and Decision Sciences companies. Through a unique ecosystem that brings together People, Processes, and Platforms, MuSigma collaborates with over 140 Fortune 500 firms. There are currently 3500 Data Scientists working for them around the world. MuSigma has been named Walmart’s Supplier of the Year on four occasions, as well as Microsoft’s preferred Analytics partner
• Fractal Analytics - Fractal is one of the most well-known Artificial Intelligence and Data Science Companies. Fractal’s objective is to employ AI and help the world’s most admired Fortune 500 firms by powering every human decision in the enterprise. Qure.ai, which helps radiologists make better diagnostic decisions, Cuddle.ai, which helps CEOs and Senior Executives make better tactical and strategic decisions, Theremin.ai, which helps investors make better investment decisions, and Eugenie.ai, which helps find anomalies in high-velocity Data, are all Fractal products.
• Bridgei2i Analytics - BRIDGEi2i is a trusted partner for businesses all around the world for driving digital transformation efforts. They solve difficult business problems with contextual AI-powered insights and achieve Digital Transformation results.
• Tiger Analytics - Tiger Analytics is breaking new grounds in how AI and Analytics may be used to solve some of the world’s most difficult problems. For several Fortune 500 organizations, they have created custom solutions based on Data and Technology. They have offices in numerous cities across the United States, the United Kingdom, India, and Singapore, as well as a large worldwide virtual workforce.
• LatentView - LatentView Analytics is one of the prominent worldwide Analytics and Data Science Companies that help businesses achieve Digital Transformation and build a competitive advantage through Data. With Analytics Solutions they provide a 360-degree perspective of the digital consumer, power Machine Learning capabilities, and aid AI ambitions.
• Absolutdata - Absolutdata, an Infogain company, combines cutting-edge AI and Machine Learning with its legacy in analytical frameworks, business expertise, and technology to create scalable business impact throughout the enterprise. The enterprise-focused NAVIK AI Platform combines AI, Data, and Analytics to serve as the intelligence layer for forward-thinking businesses. 
• Innovaccer - Innovaccer Inc is a leading Healthcare Data activation platform firm that uses cutting-edge Analytics and transparent, clean, and accurate Data to deliver more efficient and effective Healthcare. Innvoaccer’s goal is to help medical-related organizations make powerful decisions and achieve strategic goals based on key insights and predictions from their Data by simplifying complex Data from all points of care and streamlining the information. It is the only company amongst the top Data Science Companies which only focuses on Healthcare Solutions.
• TEG Analytics - TEG Analytics is a Data Science as a Service Company that helps businesses make better decisions by combining Business, Technology, and Applied Mathematics. Their goal is to provide Insights at Business Speed, hence, being an excellent Data Science Organization.
• Teradata - Teradata is a Multi-Cloud Data platform that helps businesses solve Data challenges from start to finish. Only Teradata gives you the flexibility to handle today’s enormous and mixed Data workloads, making your Data more accessible to everyone without putting your Data in danger.
• Impact Analytics - Impact Analytics creates AI-powered retail automation technologies with a 360-degree view to help businesses automate complex procedures and turn Data into insights. To create solutions that lure clients, they combine business expertise from top-tier strategy consultants, advanced Machine Learning techniques from skilled Data Scientists, and cutting-edge product development from expert Application Designers and Developers. Impact Analytics comes amongst the top companies for Data Science.

What is the compensation range for Data Scientists?

The compensation range for data scientists varies depending on several factors, such as their level of experience, location, industry, and company size. However, in general, data scientists are highly valued in the job market and can earn competitive salaries.

According to Glassdoor, the average base salary for a data scientist in the United States is around $113,000 per year.

However, this can range from around $85,000 per year for entry-level positions to over $150,000 per year for senior-level roles. Additionally, data scientists often receive bonuses, stock options, and other benefits, which can significantly increase their overall compensation. Some companies also offer relocation assistance, sign-on bonuses, and other perks to attract top talent in the field.

Overall, data science can be a highly lucrative career path with the potential for high salaries and various benefits.

What are examples of good Boolean searches for finding Data Scientists?

Some basic examples:

• “Data Scientist” AND Python AND SQL AND Hadoop – will run a search for jobs that only contain all of these keywords
• “Data Scientist” OR “Data Science” OR “Machine Learning Scientist” – will run a search for jobs that contain any of these keywords
• “Data Scientist” AND NOT “Machine Learning Engineer” – will run a search for Data Scientists jobs, but remove any that contain the keyword “Machine Learning Engineer”

You’ll notice the use of quotation marks (“”) in the above examples – this is another operator used to group phrases together. If you didn’t use the quotation marks, the site will search both words independently (for example, Data Scientist would search Data OR Scientist, whereas “Data Scientist” will search just for Data Scientist). 

Another useful operator is parentheses or brackets (). This is used to group sections of keywords together to tell the site how to break your search string down. For example:

• (“Data Scientist” OR “Data Science”) AND (Python OR R OR Matlab) – this search will search for either keyword within the first brackets with the other keywords in the other brackets. In other words, it’ll search for multiple combinations between the two strings. 

Now that we know the main operators, we can start to combine these to create our very first Boolean search string:

(“Data Scientist” OR “Data Science”) AND (Python OR R) AND NOT (“Machine Learning Engineer” OR “Developer”)

In this instance, the results will be focused on data scientist jobs that mention Python or R but will exclude machine learning or more developer oriented folks.

A generic boolean search string around terms looks like:

• -job -jobs -sample -examples, to exclude irrelevant results
• (intitle:resume OR intitle:cv) to discover candidates’ online resumes or CVs
• (“data scientist” OR “data engineer”) to cover variations of the same job title

Here’s an example of a simple string to find resumes:

(intitle:resume OR intitle:cv) (“data scientists” OR “ml engineers”) -job -jobs -sample -templates

With this search string, the words “resume” or “CV” have to appear in the page title. Adding variations of data scientists job roles provides a larger number of relevant results. And, excluding more terms will reduce false positives.

Add more criteria in your Boolean search string for data scientists to find profiles that better match your requirements. Some examples:

Skills and experience with specific software: 

1. (intitle:resume OR intitle:cv) “data scientist” Hadoop -job -jobs -sample -templates
2. (intitle:resume OR intitle:cv) “data scientist” (MATLAB OR SAS NOT SPSS) -job -jobs -sample -templates

Work (or have worked) in senior roles

1. (intitle:resume OR intitle:cv) “data scientist” (“senior data scientist” OR “data warehouse architect” OR senior) -job -jobs -sample -templates

Can code

1. (intitle:resume OR intitle:cv) “data scientist” (R OR Java) -job -jobs -sample -templates
2. (intitle:resume OR intitle:cv) “data scientist” Python-job -jobs -sample -templates

Let’s look at what a final Boolean search looks like using the following fields:

• Job title: (“Data Scientist” OR “Data Science” OR “Quantitative Analyst” OR “Quant Analyst”) AND (“Senior” OR “Lead” OR “Team Lead”)
• Sector: (“Financial Services” OR Banking)
• Risk Analytics: (“Risk modeling” OR “Risk Analytics” OR “Risk Advanced Analytics”)
• Tech Stack: Python 

The Boolean search string that can be created using the the knowledge we have gained and the aforementioned fields, applicable to any job board, would resemble the following:

(“Data Scientist” OR “Data Science” OR “Quantitative Analyst” OR “Quant Analyst”) AND (“Senior” OR “Lead” OR “Team Lead”) AND (“Financial Services” OR Banking) AND (“Risk modeling” OR “Risk Analytics” OR “Risk Advanced Analytics”) AND Python 

By using Boolean search as shown above in combination with other research methods, you can greatly increase your chances of finding the right data scientist! Good luck with your recruiting!

Any Tips for recruiting and retaining Data Scientists?

It is important to note that retaining data scientists is a key factor in recruitment due to the abundance of opportunities for this niche field.

Hence, after determining the roles, responsibilities, and skill-set required in the organization, recruiters can use the following tips to hire as well as retain the top talent:

• To retain data scientists, it's important to engage them with challenging projects and a sense of purpose. This can be achieved by assigning them cutting-edge projects or work that advances a cause. It's essential to make them feel essential to the company by giving them mission-critical projects.
• To maximize their productivity and job satisfaction, data scientists should be freed up to focus on creative work. This can be done by bringing in data engineers and machine learning engineers to handle data preparation and engineering work. It's also helpful to have data literate employees as subject matter experts on the data science team.
• Data scientists should be connected to the business team to ensure they are working collaboratively on important questions and making a measurable impact on the business.
• Building a pipeline of tools and talent is important. Smart software should be implemented to handle low-level and repetitive tasks, and partnerships with universities and colleges should be considered to create a pipeline of trained interns and new graduates to beef up the data science support team.
• Data scientists should be given continuous education and training to keep them up to date with the latest advances in data science techniques and tools.

Finally, it's important to compensate data scientists well to hire and retain them. Competitive compensation and benefits packages, such as remote work and flexible hours, should be offered to keep them satisfied and engaged.

Conclusion

It's important to understand that Data Science is a broad field and data scientists work with various disciplines such as Artificial Intelligence (AI), Machine Learning (ML), Natural Language Processing (NLP), or a combination of these based on the needs of the organization. As mentioned, we have detailed guides to Hiring AI Engineers, Hiring Machine Learning Engineers and Hiring NLP Engineers that might also be helpful in your hiring. Good luck!

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