Guide to Hiring Data Professionals
We interact with various digital touch points throughout our day. Every time we click, email, do a blog post, and much more, we generate data. Understanding and leveraging the wealth of data is no doubt a key concern for every organization today.
To derive insights from these massive datasets, professionals with specialized skillsets are required. This is the key reason for the surge in demand for data specialists including data scientists and data engineers in the talent market. Presently, the demand for these professionals outweigh the supply.
Difference between Data Scientist and Data Engineer
There are many criteria for understanding the difference between data scientist and data engineer. But first, we must develop a basic understanding of what the two job positions entail.
A data scientist cleans, polishes and organizes (big) data. They create advanced analytics along with machine learning models and artificial intelligence. A data engineer is responsible for the creation, development, construction, testing, and maintaining of architectures including databases and large-scale processing systems.
esides hard skills, soft skills are equally important for both these professionals are employed by companies for augmenting their IT infrastructure by adding a human, unique perspective coupled with specialized skill sets.
Data Scientists v. Data Engineers: Hard and Soft Skills Comparison
Data Scientists v. Data Engineers: Key Job Skills Comparison
| Data Scientist | Data Engineer | |
|---|---|---|
| Job Function |
Drawing insights from raw data for deriving value from data using statistical models |
Creates APIs and frameworks to consume data from various sources |
| Key Responsibility | Optimizing performance of machine learning/statistical model | Optimizing performance of entire data pipeline |
| Majors |
Math, applied statistics, operations research, computer science, physics, aerospace engineering |
Computer science, engineering |
| Average Starting Salary (PA) | $115k | $100k |
| Industries Hiring | Any industry in which large amounts of data are analysed | Any industry in which large amounts of data are collected and stored |
| Ranked (compensation wise) | Highest | Middle |
RulesIQ’s Six Rules for Hiring Data Scientists
RulesIQ’s Recruitment Best Practices
RulesIQ believes that successful people make successful companies. Here are five recruitment best practices that we follow:
1. Goal Setting: We believe that companies looking to hire talented data professionals must first establish the goals the candidate has to fulfil and the resources they can offer to help them in meeting those goals.
2. Setting Job Requirements: We work with our partners (clients) to ensure that job requirements are communicated clearly right away. This ensures that candidates who are a poor fit are filtered right at the outset.
3. Focus: We focus on the following key areas to shortlist the best data professionals – technical skillsets, pace of work, and communication.
4. Relevance of Analytics: We make sure that our candidates know that our clients understand the relevance of analytics for an organization.
5. Budget Allocation: We ensure that the candidates are apprised that our clients have adequate budget for purchasing the right tools and, if required, are willing to hire supporting professionals like data engineers or product managers in their job roles.
