Best practices for hiring data science teams

Best practices for hiring data science teams

First, I have to mention one line from โ€˜the Rime of the Ancient Marinerโ€™ poem, where Samuel Taylor Coleridge beautiful wrote the lines

Water, water, everywhere,ย 

But not a drop to drink.

Now, we are seeing the same situation in the case of Data Science. Data Science is everywhere, but we could not find skilled manpower or experts in this area. The HR team having a hard time to select or find a suitable person for the same. Despite being surrounded by Big data analytics courses and training; we could not get any benefit from it to find a suitable candidate. However, we recommend to HR teams to at least go through my recommendations.

Recommendations:

As we are aware as a recruitment or hiring personnel that Data Science is undeniably and undoubtedly is one of the most sought after skill in todayโ€™s world. It focuses on developing new insights and understanding of business performance based on data science techniques. It makes extensive use of data, statistical and quantitative analysis.

For hiring data science teams, we have to consider these following factors from each individual especially team leader that they should have two separate qualities.

  1. Technical Skills
    1. Programming languages like R and Python
    2. Analytics tools like SPSS, Tableau, SAS
    3. Another tool: Hadoop, Spark, Matlab
    4. Mathematical and Statistical Ability
  1. Non-Technical Skills
    1. Communication
    2. Creativity and Analysis
    3. Inquiry Skills
    4. Engineering-Design Thinking.
    5. Critical Thinking.
    6. Collaboration.

Apart from the above skills, we go to find a different skill set as I mention in this Table.

Soft Skills

Soft Skills Data Analytics Skills Tools & Technologies Technologies Skills
ยทย ย  Active Learning

ยทย ย  Business Analytics

ยทย ย  Business Intelligence

ยทย ย  Coding / Programming

ยทย ย  Collaboration

ยทย ย  Communication

ยทย ย  Creative and Critical Thinking

ยทย ย  Generating Hypotheses

ยทย ย  Interpersonal Skills

ยทย ย  Judgement

ยทย ย  Organization Network Analysis

ยทย ย  Perceptiveness

ยทย ย  Problem Solving

ยทย ย  Software Engineering

ยทย ย  Big Data

ยทย ย  Data Analysis

ยทย ย  Data Architecture

ยทย ย  Data Governance Policies

ยทย ย  Data Intuition

ยทย ย  Data Maturity Model

ยทย ย  Data Mining

ยทย ย  Data Modeling

ยทย ย  Data Presentation

ยทย ย  Data Quality

ยทย ย  Data Science

ยทย ย  Data Security

ยทย ย  Data Visualization

ยทย ย  Data Warehousing

ยทย ย  Data Wrangling

ยทย ย  Hadoop

ยทย ย  MOOCs

ยทย ย  MS Power BI

ยทย ย  NoSQL

ยทย ย  Python Programming

ยทย ย  QlikView

ยทย ย  R Programming

ยทย ย  SAP

ยทย ย  SAS

ยทย ย  SPSS

ยทย ย  SQL

ยทย ย  Stata

ยทย ย  Tableau

ยทย ย  VB

ยทย ย  Open Source

ยทย ย  LaTeX

ยทย ย  Ability to Link Data Across Programs and Platforms

ยทย ย  Alternate Data Collection Approaches (Beyond Typical Survey)

ยทย ย  Blockchain

ยทย ย  Comparative Trend Analysis

ยทย ย  Content Analysis

ยทย ย  Customer Analytics

ยทย ย  Deep Learning

ยทย ย  Sentiment Analysis

ยทย ย  Text Mining

ยทย ย  Virtual Reality

ยทย ย  Analysis and Design

Technical Skills

Industry Knowledge Maths & Stats Skills Additional Skills Other Skills
ยทย ย  Start-ups

ยทย ย  Automobile

ยทย ย  Banking & Financial Services

ยทย ย  E-Commerce

ยทย ย  Education

ยทย ย  Energy and Utilities

ยทย ย  Environment

ยทย ย  Healthcare

ยทย ย  Oil and Gas

ยทย ย  Retail

ยทย ย  Social Media

ยทย ย  Telecom

ยทย ย  Travel Industry

ยทย ย  Differential Calculus

ยทย ย  Forecasting and Predictive Analysis

ยทย ย  Linear Algebra

ยทย ย  Mathematical Modeling

ยทย ย  Probability

ยทย ย  Qualitative Analysis

ยทย ย  Quantitative Analysis

ยทย ย  Risk Analysis

ยทย ย  Statistical Analysis

ยทย ย  Statistical Learning

ยทย ย  Statistical Modeling

ยทย ย  Statistics Sampling

ยทย ย  Capability of Team to Turn Data into Usable Insights

ยทย ย  Common Data Definitions

ยทย ย  Decision Making Process

ยทย ย  Human Resources and Business Data Integration

ยทย ย  Management Support and Organization Structure

ยทย ย  Systematic Methodology

ยทย ย  Transparency in how Data will be used

ยทย ย  Strategic and Planning Research Advisory

ยทย ย  Applications: ArcGIS, Maximo, AutoCAD

ยทย ย  Database: Oracle, SQL, Access, FoxPro, Dbase

ยทย ย  Familiarity: MS Office, MS Project, Visio

ยทย ย  Networking: Novell, Windows, and Linus

ยทย ย  Programming: C, C++, VC++, RPG/III, Dbase, Cobol, & Pascal

ยทย ย  Tools: MS-Query, Rumba/400, Q&E, Crystal Reports, SourceSafe

By:ย Dr. Mohammed Khursheed Akhtar

 

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