Skillsets you need to crack that data scientist jobThe demand for data scientists is increasing with companies investing in data more than ever. The applications in the field of data science are endless and so are its uses. There’s no doubt that businesses crave good data scientists today. While there are a good number of data scientists and data graduates in the market, it is not easy finding just the right one for your team.

ETCIO spoke with top analytics leaders and influencers to understand what skills should a leader look for in data scientists while hiring them.

Technical skills

Data scientists will be working on your company’s database, performing analysis and modeling the data. They should possess the following skills

1. Strong programming skills, mathematics and statistics

Mathematics and statistics are the basics of data science. Data science begins with statistics and then advances. Further, to perform analysis and modelling with data, data scientists must integrate multiple data sources, clean the data, and aggregate it, which requires solid programming ability.

2. Aptitude for data

Data scientists work with huge amounts of data and complex problems hence it is important for them to understand and simplify the key insights and messages for everyone’s consumption.

“Data scientists need to understand the limitations of data, perform hypothesis driven analysis, and visualize data to generate insights and develop the overall solution. This requires a strong aptitude for data exploration and visualization,” said Sriram Krishnamurthy, Head of Data Science, Tiger Analytics

3. Understanding of model development and algorithms

Data scientists need to try out various models to find the best and simplest model to solve complex business problems.The key role of a data scientist is to use algorithms and data to solve business problems. To do so, they need to have a deep understanding of model development and the ability to evaluate models from both a business and technical perspective.

“Core technical skills including, Data Engineering & ETL, SQL development, Data Visualisation, Algorithms, Analytics and Modeling, Machine Learning Methods. Adding to that, knowledge of analytics tools and technologies such as Python, R, Power BI, Qlik Sense, SAP HANA, SQL Server, Azure, Databricks, Synapse, MLOps, Excel, VBA, C#, Macro, IDE (like Visual Studio), libraries, etc, are also foundational to the rigorous application of data science in solving business problems,” said Rajiv Dwivedi – Senior Director, Business Operations Management, Lam Research India.

Soft skills

4. Communication skills

While data scientists are expected to be strong in technical skills using algorithms and python , one big challenge they face is communicating the impact and results.

“For this reason, having good communication skills is imperative with two key aspects – a. ability to explain the method, usage and impact to business stakeholders in an easy to understand business language ( without much jargon ). b. understanding the project scope and translating it well defined analytics problem statements and setting the business expectations,” said Saurabh Agarwal, SVP- Analytics & ML, Lenskart.

5. Business acumen

A data scientist should also have business acumen and an understanding of business processes which is key to success in this role. The ability to collaborate with cross-functional teams, communicate the results of the analysis to other team members and as well as to the key stakeholders and decision-makers who need to be able to quickly understand the key messages and insights are vital, as it impacts business results.

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