Data Science Jobs
1. Data Analyst:
Data
analysts are responsible for a variety of tasks including visualization,
munging, and processing of massive amounts of data. They also have to perform
queries on the databases from time to time. One of the most important skills of
a data analyst is optimization. This is because they have to create and modify
algorithms that can be used to cull information from some of the biggest
databases without corrupting the data.
Requirement:
SQL, R, SAS, Python are some of the sought after
technologies for data analysis.
2. Data Engineers:
Data engineers
build and test scalable Big Data ecosystems for the businesses so that the data
scientists can run their algorithms on the data systems that are stable and
highly optimized. Data engineers also update the existing systems with newer or
upgraded versions of the current technologies to improve the efficiency of the
databases.
Requirement:
Technologies
that require hands-on experience include Hive, NoSQL, R, Ruby, Java, C++, and
MATLAB.
3. Database Administrator:
The job
profile of a database administrator is pretty much self-explanatory- they are
responsible for the proper functioning of all the databases of an enterprise
and grant or revoke its services to the employees of the company depending on
their requirements. They are also responsible for database backups and
recoveries.
Requirement:
The
essential skills and talents of a database administrator include database
backup and recovery, data security, data modeling, and design, etc.
4. Machine Learning Engineer:
Machine
learning engineers are in high demand today. However, the job profile comes
with its challenges. Apart from having in-depth knowledge in some of the most
powerful technologies such as SQL, REST APIs, etc. machine learning engineers
are also expected to perform A/B testing, build data pipelines, and implement
common machine learning algorithms such as classification, clustering, etc.
Requirement:
Firstly,
you must have a sound knowledge of some of the technologies like Java, Python,
JS, etc. Secondly, you should have a strong grasp of statistics and
mathematics.
5. Jr. Data Scientist:
They mostly
involve being able to work in a team, possessing a passion for data science and
data analysis, creating specific systems, and tracking how they perform over
time, data mining, and so on.
Requirement:
The mandatory skills: Statistics, Programming and Communication
“Other” skills:
Level of
education (MS & Ph.D. are highly preferred)
Professional
development (workshops, MOOCs, certifications, etc.)
Project
portfolio (proof of analytics applications)
Work
experience (proof of the 3 mandatory skills in the industry setting)
Results
(proof of finishing a project in your project portfolio or work experience)
6. Data Scientist:
Data scientists
have to understand the challenges of business and offer the best solutions
using data analysis and data processing. For instance, they are expected to
perform predictive analysis and run a fine-toothed comb through
“unstructured/disorganized” data to offer actionable insights. They can also do
this by identifying trends and patterns that can help the companies in making
better decisions.
Requirement:
You have to
be an expert in R, MatLab, SQL, Python, and other complementary technologies.
The main skills needed for a data scientist job:
Coding —
R/Python, SQL, Excel, Tableau
Statistics
— Basic stats and math like mean, median, averages, statistical differences,
chi-square tests, etc.
Domain
Knowledge
7. Data Architect:
A data
architect creates the blueprints for data management so that the databases can
be easily integrated, centralized, and protected with the best security
measures. They also ensure that the data engineers have the best tools and
systems to work with.
Requirement:
Data
architecture requires expertise in data warehousing, data modeling, extraction
transformation, and load (ETL), etc.
8. Statistician:
A
statistician, as the name suggests, has a sound understanding of statistical
theories and data organization. Not only do they extract and offer valuable
insights from the data clusters, but they also help create new methodologies
for the engineers to apply.
Requirement:
A
statistician has to have a passion for logic. They are also good with a variety
of database systems such as SQL, data mining, and various machine learning
technologies.
9. Business Analyst:
They do
have a good understanding of how data-oriented technologies work and how to
handle large volumes of data, they also separate the high-value data from the
low-value data. In other words, they identify how Big Data can be linked to
actionable business insights for business growth.
Requirement:
They should
have an understanding of business finances and business intelligence, and also
the IT technologies like data modeling, data visualization tools, etc.
10. Data and Analytics Manager:
A data and
analytics manager oversees the data science operations and assigns the duties
to their team according to skills and expertise. Their strengths should include
technologies like SAS, R, SQL, etc. and of course management.
Requirement:
They must
have excellent social skills, leadership qualities, and an out-of-box thinking
attitude. You should also be good at data science technologies like Python,
SAS, R, Java, etc.
11. Data Science Manager:
Data
science managers need to be good managers in general. A good manager has a
vision, is goal-oriented, cares for the team, listens to them for making
decisions, is a mentor and coach, empowers and inspires team members, and
avoids micromanagement.
Requirement:
They must
have excellent social skills, leadership qualities, and an out-of-box thinking
attitude. Data Science Manager possesses all the elements to make informed
decisions about building a data-centric product.
12. Data Science Consultant:
A
successful data science consultant requires a wide range of skills, including
domain knowledge, business acumen, analytical thinking & problem-solving,
teamwork & project management, communication & presentation, machine learning,
big data, and software development.
Requirement:
Excellent
understanding of machine learning techniques and algorithms, such as k-NN,
Naïve Bayes, SVM, Decision Forests, etc. Experience with common data science
toolkits, such as R, Weka, NumPy, MATLAB, etc.
Proficiency
in using query languages such as SQL, Hive, Pig
Great
communication skills
Experience
with NoSQL databases, such as MongoDB, Cassandra, HBase
Good
applied statistics skills, such as distributions, statistical testing,
regression, etc.
Data-oriented
personality
13. AI Consultant:
They have
the ability to design, build, and deploy predictive and prescriptive models
using statistical modeling, machine learning, and optimization. Ability to use
structured decision-making to complete projects. Ability to manage an entire ML
project from business issue identification, data audit to model maintenance in
production.
Requirement:
Ability to
solve complex business challenges
Ability to
design, build and deploy predictive and prescriptive models
Ability to
use structured decision-making to complete projects
Ability to
manage an entire ML project from the business issues and many more.